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The objective of this paper is to classify recently released Indian coins of different denomination. The objective is to recognize the coins and count the total value of the coin in terms of Indian National Rupees (INR). The system designs coin recognition which uses by combining Robert’s edge detection method, Laplacian of Gaussion edge detection method, Canny edge detection method and Multi-Level Counter Propagation Neural Network (ML-CPNN) based on the coin Table 1. In this paper, it is proposed to introduce ML-CPNN approach. The features of old coins and new coins of different denominations are considered for classification. Indian Coins are released with different values and are classified based on different parameters of coin such as shape, size, surface, weight and so on. Some countries’ coins are having same parameters, but with different value. This paper concentrates on affine transformations such as simple gray level scaling, shearing, rotation etc. The coins are well recognized by zooming processes by which a coin size of the image is increased. To implement the coin classification, code is written in Matlab and tested with simulated results. A method is proposed for realizing a simple automatic coin recognition system more effectively. The Robert’s edge detection method gives 93% of accuracy and Laplacian of Gaussion method 95% of the result, the Canny edge detection method yields 97.25% result and the ML-CPNN approach yields 99.47% of recognition rate.