Back propagation neural networks have been widely used as classifiers in many complex classification tasks. However, early experimental results show that as the number of classes involved in a classification task increases, the classification accuracy of these networks decreases, especially in the presence of noisy inputs. In addition, larger size networks are needed to be utilized in such cases, a fact that may not always be possible. In order to overcome both of these undesirable effects a new approach is proposed in this paper which utilizes multiple, relatively small size networks to perform the classification task. This approach has been applied on a machine printed character recognition experiment and it has demonstrated better classification accuracy than the one exhibited by the single, larger size, network approach.