Existing neuro-fuzzy clustering algorithms suffer from either restrictions in the shape of the clusters formed or true integration of fuzzy learning rule into neural network processing. An Integrated Adaptive Fuzzy Clustering (IAFC) algorithm using a structure, similar to that found in the Adaptive Resonance Theory (ART-1) neural network, is presented. The IAFC incorporates a new learning rule and a new similarity measure to eliminate some structural problems inherent to other fuzzy ART-type neural networks. The new learning rule utilizes a fuzzy membership value, a function of the number of iterations, and a fuzzy within-cluster membership value. The new similarity measure incorporates a fuzzy membership value to the Euclidean distance. The incorporation of the new learning rule and the new similarity measure guarantees the convergence of weights in the IAFC algorithm and provides more flexibility to the shapes of the clusters formed by this algorithm. The critical parameters in the operation of IAFC are discussed. The performance of IAFC is evaluated in classification of real data and compared with other recent neuro-fuzzy clustering algorithms.