Processes governing patterns of richness of riverine fish species at the global level can be modelled using artificial neural network (ANN) procedures. These ANNs are the most recent development in computer-aided identification and are very different from conventional techniques(1,2). Here we use the potential of ANNs to deal with some of the persistent fuzzy and nonlinear problems that confound classical statistical methods for species diversity prediction. We show that riverine fish diversity patterns on a global scale can be successfully predicted by geographical patterns in local river conditions. Nonlinear relationships, fitted by ANN methods, adequately describe the data, with up to 93 per cent of the total variation in species richness being explained by our results. These findings highlight the dominant effect of energy availability and habitat heterogeneity on patterns of global fish diversity. Our results reinforce the species-energy theory(3) and contrast with those from a recent study on North American mammal species(4), but, more interestingly, they demonstrate the applicability of ANN methods in ecology.