Abstract The designer of a pattern classification system is often faced with the following situation: finite sets of samples, from the various classes, are available along with a large set of measurements, or features, to be computed from the patterns. In pattern recognition literature, several investigations have demonstrated the relationship between dimensionality, sample size and recognition accuracy. The ultimate goal of this paper is to study the generalization error of statistical (1_NN, 4_NN), fuzzy (FCM, FKCN), neural (MLPNN, RBFNN) and neuro-fuzzy classifiers in high dimensional spaces. Computational complexity of classification algorithms in high dimensional spaces is also discussed. Our experimental results show the robustness of fuzzy, neural, and neuro-fuzzy classifiers to the curse of dimensionality.