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Cluster analysis, a set of tools for building groups from multivariate data objects is extensively applied in many fields. One of the most widely used classical approaches of clustering is K-means algorithm. Kohonen's Self Organizing map is a neural network clustering methodology that maps an n-dimensional input data to a lower dimensional output map. In this study, we have compared K-means algorithm with Self Organizing map on a real life data with known cluster solutions. The performance of these algorithms is examined with respect to changes in the number of clusters and number of observations. Misclassification rates and point biserial correlation are used to compare performance of both the methods.