This paper presents a modular, unsupervised neural network architecture which can be used for clustering and classification of complex data sets. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a conventional fuzzy K-means clustering algorithm as a learning rule embedded within a control structure similar to that found in the adaptive resonance theory (ART-1) network. AFLC adaptively clusters analog inputs into classes without a priori knowledge of the entire data set or of the number of clusters present in the data. The classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. It is shown that the definition of the distance metric can be adjusted as necessary to fit the characteristics of the input data. The AFLC algorithm using two different distance definitions is discussed and then the operating characteristics are described. The performance of the algorithm is presented through application of the algorithm to clustering computer generated normally distributed data, the Anderson & Fisher Iris data, and data generated from projections of 3-D objects in constrained motion.