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Artificial neural networks are one of the widely used automated techniques. Though they yield high accuracy, most of the neural networks are computationally heavy due to their iterative nature. Hence, there is a significant requirement for a neural classifier which is computationally efficient and highly accurate. To this effect, a modified Counter Propagation Neural network (CPN) is employed in this paper, which proves to be faster than the conventional CPN. In the modified CPN model, there was no need of training parameters because it is not an iterative method like back-propagation architecture which took a long time for learning.