In this paper, we examine the performance of fuzzy clustering algorithms as the major technique in pattern recognition. Both possibilistic and probabilistic approaches are explored. While the Possibilistic C-Means (PCM) has been shown to be advantageous over Fuzzy C-Means (FCM) in noisy environments, it has been reported that the PCM has an undesirable tendency to produce coincident clusters. Recently, an extension of the PCM has been presented by Timm et al., by introducing a repulsion term. This approach combines the partitioning property of the FCM with the robust noise insensibility of the PCM. We illustrate the advantages of both the possibilistic and probabilistic families of algorithms with several examples and discuss the PCM with cluster repulsion. We provide a cluster valid-ity function evaluation algorithm to solve the problem of parameter optimization. The algorithm is especially useful for the unsupervised case, when labeled data is unavailable.