A large number of applications requires classifying or grouping data into a set of categories or clusters. Most popular clustering techniques to achieve this objective are K-means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. In this paper, a clustering method based on the use of a niching algorithm is presented, with the aims of finding the best compromise between the inter-cluster distance maximization and the intra-cluster distance minimization. This method is applied to a standard test problem and to the classification of railway driving missions.