This paper presents a multiple classifier approach as an alternative solution to the closed-set text-independent speaker identification problem. The proposed algorithm which is based on Dempster-Shafer theory of evidence computes the first and Rth level ranking statistics. Rth level confusion matrices extracted from these ranking statistics are used to cluster the speakers into model sets where they share set specific properties. Some of these model sets are used to reflect the strengths and weaknesses of the classifiers while some others carry speaker dependent ranking statistics of the corresponding classifier. These information sets from multiple classifiers are combined to arrive at a joint decision. For the combination task, a rule-based algorithm is developed where Dempster's rule of combination is applied in the final step. Experimental results have shown that the proposed method performed much better compared to some other rank-based combination methods. (C) 2003 Elsevier B.V. All rights reserved.