Clustering is the unsupervised classification of data items into homogeneous groups called clusters. Clustering algorithms are computationally intensive, particularly when they are used to analyze large amounts of data and this is the case in many pattern recognition, image analysis applications. A possible approach to reduce the processing time is based on the implementation of clustering algorithms on scalable parallel computers. This paper describes the design and implementation of P-AFLC, a parallel version of the Adaptive Fuzzy Leader Clustering system based upon the competitive learning model for determining optimal classes in large data sets. The system architecture, its implementation, and experimental performance results are reported, together with theoretical performance evaluation.