This aper introduces the multiple Neocognitron module approach for the effective target reognition. The Neocognitron which is designed to classify a pattern by extracting the local features from it, seems to be an unique method that can perform a pattern recognition using the neural networks. But due to its rigid structure, the Neocognitron must be reconstructed whenever there exists a variation on the number of classes. This is a quite difficult problem for the target recognition application that needs huge amount of computation and numerous classes to be classified. In this paper, we construct several smaller Necognitrom modules and train each module to adapt each class. After construction of the mulules, we integrate them in parallel so as to adaopt input at the same time and to produce each score that shold be matched to be learned class. This approach can reduce the sizes of the networks and is adaptive to the increase of classes as well as the authentic distortion, shift, scale variation and slight rotation invariant properties of general Neocognitron. This paper show the effectiveness of the proposed approach through some experience and performs analysis of the inhibitory interconnections in the architecture of the multiple module structure.