Support Vector Machines (SVMs) have been recently proposed for pattern recognition. Their basic property allows us to find a decision surface between two classes in terms of a hyper plane in a high dimensional space. In a multiclass recognition problem, SVMs are used in the form of a combination of binary classifiers. However, SVMs are unable to retrieve the top N matches, since they are designed to yield only one - the best match - in a multi-class problem. In other words, there is no proper similarity measurement for ordering all the classes in a given space using SVMs. In this paper, we present an efficient method for the retrieval of the top N matches in a multiclass problem using SVMs. For evaluation of the proposed method, we compared its result with that of a PCA algorithm in ranking the matches between classes.