Object recognition has been the subject of research interest in the last decade. Object recognition in traffic signals has been done with incorporation of most image processing techniques to enhance image. This processing of image involves the utilization of neural networks. So the sole purpose of this paper is to identify which neural network could bring in the great storage efficiency, quality, robustness, pattern completion, content addressable memory of the image (objects) recognition in the traffic signal systems. So, the extensive comparison has been done on these neural networks for the application. Most of the pattern mapping neural networks suffer from the drawbacks that during learning of weights, the weigh matrix tends to encode the presently active pattern, thus weakening the trace of patterns it had already learnt. The other problem that the common types of neural networks face is the forceful categorization of a new pattern to one of the already learnt classes. On occasions such categorization seems to be ridiculous as the nearest class of current pattern may be significantly different with respect to the center of the class. The problems of the lack of stability of the weight matrix and forceful categorization of a new pattern to one of the existing classes, has led to the proposal of a new architecture for pattern classification.

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Video Surveillance Adaptive Resonance Network Artificial Neural Networks Plasticity-stability Dilemma Object Recognition