Most studies on combining multiple classifiers have focused on combination methods, but a few studies have investigated on how to select component classifiers from a classifier pool. Multiple classifier systems performance varies with the component classifiers as well as the combination method. In this paper, methods based on information theory are proposed for selecting component classi- fiers, provided that the number of component classifiers is fixed in advance. These methods are applied to the classifier pool and examine the possible classifier sets. The system is compared to other multiple classifier systems on the recognition of unconstrained handwritten numerals.