The performance of multiple classifier systems varies with the performance of component classifiers as well as the method of combination. In this paper, information-theoretic methods are proposed for constructing multiple classifier systems, provided that the number of component classifiers is constrained in advance. These proposed methods are applied to a classifier pool and examine the possible classifier sets by the selected information-theoretic criteria. One of them is then selected as the candidate and is evaluated together with the other multiple classifier systems on the recognition of unconstrained handwritten numerals from Concordia University and the University of California, Irvine. Experimental results support the approach.