Networks of fuzzy basis functions (FBF) characterized by singleton fuzzifier, Gaussian membership functions, product- inference, and height method defuzzifier, show interesting characteristics, including the approximation of the Bayes discriminant function. In this paper, a classifier based on a simplified FBF (SFBF) network is presented and its performances are studied in the frame work of handwritten digits recognition. The learning rules of the SFBF network are less complex than those of a FBF network, and experimental results show a significant speed-up of learning, at the cost of a small decrease of the generalization performances. Moreover, a hybrid pattern recognition scheme (HS) is proposed, based on a hierarchy of a SFBF network plus a nearest-neighbor rule (NR), that recognizes the patterns rejected by the SFBF network. This approach permits us to recover the loss in generalization exhibited by the SFBF network alone. Specifically, the efficiency of the hierarchy can be improved, since the output of SFBF network for a rejected test pattern can be used to edit the set of rejected (training set) patterns: the NR searches only for patterns belonging to classes that get the highest rates by the SFBF network.