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In this research a computer vision-based shape recognition system which combines an image projection and the back propagation neural network is proposed. The system uses the image projection and features of the projection histogram to represent the shape, and uses the back propagation neural network to classify the shape. In this research, a fast normalization scheme is developed which is especially designed for image projection and can also find the same projection axis no matter what the object's orientation. Therefore, the proposed system can be invariant to scale, translation, and orientation variations. This research finds that the direct representation scheme of the back propagation neural net has better accuracy than the binary coding representation scheme. This research also finds that the proposed system's classification accuracy is greater than 98.8% and its processing speed is also very fast. Therefore, the proposed system is feasible for use in shape classification, in practice.

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