The contribution of this paper is twofold. First we investigate the use of the confusion matrices in order to get some insight to better define perceptual zoning for character recognition. We will see that this idea can be used as a tool to support the design of zoning. The features considered in this work are based on concavities/convexities deficiencies, which are obtained by labeling the background pixels of the input image. Four different perceptual zoning (symmetrical and nonsymmetrical) are discussed. Experiments show that this mechanism of zoning could be considered as a reasonable alternative to exhaustive search algorithms. The second contribution is a methodology to define metaclasses for the problem of handwritten character recognition. The proposed approach is based on the disagreement among the characters and it uses Euclidean distance computed between the confusion matrices. Through comprehensive experiments we demonstrate that the use of meta-classes can improve the performance of the character recognition system.