This study reports on an investigation of the phytogeographical patterns retrieved within the exceptionally species rich Cape Floristic Region (CFR), a global biodiversity hotspot in South Africa. Modern Analytical techniques, including a novel approach developed within this study, were used to identify Phytogeographical Centres. Moreover, the efficiency and optimality of these techniques were tested against each other using several different datasets. Endemism and species richness in the core CFR were assessed against contemporary environmental conditions, using a spatially sensitive regression technique. A combined dataset of 4414 taxa was analysed, of which 4000 were recorded in the general CFR area. This represented 44.4% of the 9087 total taxa recorded in the CFR, and the largest dataset examined to date on floristic patterns in the CFR. The combined data (Combined Dataset), a relatively representative sample of the CFR flora, was used to establish overall phytogeographic patterns of endemism. Further, derivative subsets, based predominantly on phylogeny/taxonomy including taxa from eleven plant families, and two ecological guilds, Geophytes and Red Data Listed (RDL) Taxa, were also analysed. Differences between the derivative datasets revealed insights into taxonomically distinct floristic patterns, determined by the dominance of particular floristic/biotic elements within each dataset. This helped explain phytogeographical differences between previous CFR phytogeographical studies, which focused on different floristic/biotic elements. In the Combined Dataset, nearly all Quarter Degree Square (QDS) cells were assigned to PCs in the core CFR, indicating endemism is common throughout the CFR. However, endemic taxa were concentrated in the high winter rainfall west, and southwest areas. The large size of the dataset, and detailed analyses revealed additional finer phytogeographical sub-division, not previously recorded, including six Phytogeographical Provinces, 16 Centres, and 36 Sub-Centres; compared to five equivalent Phytogeographical Provinces and nine equivalent Centres of Weimarck. Hierarchical analyses of the Combined Dataset displayed congruent patterns to the previous two comprehensive phytogeographical studies of Goldblatt and Manning, (2000) and Weimarck (1941). Within the core CFR phytogeographical provinces, common patterns across studies included choria being strongly associated with TMS mountain ranges. This highlighted the importance of substrate and topography explaining floristic patterns, consequent PC formation, and the numeric dominance of montane TMS 'fynbos' taxa. The latter was confirmed through habitat analysis of endemic taxa. However, PC development was also noted on the lower lying areas, notably the Agulhas Plains, and the lowland areas neighbouring the mountains of the Southwest Phytogeographical Province, and to a lesser extent, the Northwest Province. The five analytical methods used to determine candidate PCs were evaluated for performance optimality. Bell Shaped Curve Weighting using UPGMA proved marginally more optimal than the other four methods, but differences between various weighting and clustering algorithms were less than anticipated. However, weighting was seen to be substantially better than not weighting. The novel technique of using a multiple clustering analysis approach was found to highlight areas of conflict, where floristic/biotic elements overlapped, and to supplement occasional poorly resolved trees. In addition, the approach of using GIS interrogation of candidate centres, substantially enhanced the endemic composition and size of PCs, and is strongly recommended. Moreover, post clustering GIS analyses may correct any marginal disadvantages of any single approach. Significant correlations between endemism and PC size, and richness of non-endemics and PC size were found. However, certain PCs still contained more taxa than predicted by area alone, while others contained fewer. In these PCs with over- or under-represented richness, alternative explanations (either historical and/or environmental) were required to explain why these PCs differ from other PCs in the CFR, which was subsequently investigated. Analyses of patterns of endemism and taxon richness against contemporary climatic and environmental variables using a regression technique which accounted for spatial variation in parameter estimates highlighted the importance of both the energy-water hypothesis, and the habitat heterogeneity hypothesis in the CFR. Potential evapo-transpiration and various measures of topographic heterogeneity provided much explanatory power. In addition, length of growing/rain season was particularly important in the western winter rainfall portion of the CFR, and is here retrieved for the first time as a strong predictor variable. Different input units and different floristic components of the dataset required different models for optimisation. The models were adequately able to account for much of the variability in richness (~ = 78.3- 91.2%). The Cape Floristic Region (CFR) -a global hotspot- proved an excellent area to study patterns of endemism, taxon richness, floristic patterns, and how environmental conditions affect richness because of its exceptionally high concentration of both richness because of its exceptionally high concentration of both richness and endemism, and high levels of beta and gamma diversity over relatively short geographic distances.