The Hypercolumri (HCM) neural network model is an unsupervised
competitive network consisting of hierarchical layers of the Hierarchical Self-Organizing Map (HSOM) neural networks arranged by similar to the cell planes in the Neocognitron (NC) neural network. The HCM model combines the advantages of both the HSOM and the NC while rejecting their disadvantages, and alleviates many difficulties associated with image recognition applications. It can recognize images with variant objects size, position, orientation, and spatial resolution. However, due to the hierarchical structure of the HCM model, the network spends a long tirne in the recognition. In this paper, the HCM model is introduced with a new competitive algorithm that reduces the network recognition tinie into a realtime range. The proposed algorithm uses the subset frorri the most discriminate codebook of the network weights to find the winner of each HSOM in the hrst layer of the HCM model.