The task of finding a good solution for an image processing problem is often very complex. It usually depends on the knowledge, experience and intuition of an image processing specialist. This complexity has served as a motivation for some research groups to create techniques for automatically designing image operators based on a collection of input and output examples of a desired operator. The multiresolution approach [1] has been succesfully used to statistically design W-operators for large windows. However, the success of this method directly depends on the adequate choice of a pyramidal window structure, which is used to aid in the estimation of the conditional probability distributions for patterns that do not appear in the training set. The choice is made by the designer, based on his intuition and previous knowledge of the problem domain. In this work, we investigate the use of the conditional entropy criterion for automatically determining a good pyramid. In order to compute the entropy, we have developed a technique that uses the multiresolution pyramidal framework as a model in the estimation of the joint probability distribution. The performance of the method is evaluated on the problem of handwritten digits recognition. Two different databases are used, with good practical results. Another important contribution of this work is the experimentation with resolution mappings from image pyramids theory in the context of multiresolution W-operator design.