Several novel modes of compulation have recently emerged that are collectively known as soft computing. Neurocowputing with it's Artificial Neural Networks, is one of the major components of this approach. Many artificial neural networks models mete developed to suit a wide range of applications. Among them is visual pattern recognition and classification. One of the relatively new handwritten character recognition artificial neural networks is the Xeocognitron. The Neocognitron is a hierarchical feed-forward artificial neural network that can be trained either by leaniing-with-a-teacher (supervised) method or learning-without-a-teacher (unsupervised) method. However, different Neocognitron structures exists depending on the set to be classified and the method of training. Pattern recognition in this network is performed on the basis of similarity in shape between patterns and it was reported that the pattern recognition in the Neocognitron is not affected by deformation, nor by changes in size, nor by shills in the position of the input patterns. This work is not aimed towards designing a new Neocognitron model nor developing an existing one. One of the main goals of this work is to build and evaluate the performance of three different proposed Neocognitron structures with respect to the above mentioned features using a personal computer. The first two models are used to recognize the ten Arabic handwritten numerals, 0 to 9, and the last is used to recognize an alphanumeric handwritten set including the ten Arabic numerals and the capital letters A through Z. All of these three presented models are trained by learning-with-a-teacher method. It is also an aim of this work to study the parameters that play an important role in endowing the network recognition capability and can affect it's performance. The implementation of the Neocognitron on a PC is a challenging task that requires many programming techniques since the Neocognitron memory and processing requirements are relatively high and all of the presented models were originally implemented on minicomputers or workstations. Thus the method of implementation will be discussed in details.