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In this paper, we present an off-line signature recognition and verification system using global and grid features of the signatures. An artificial neural network based on back propagation algorithm is used for recognition and verification. Performance measures like the learning rate FAR and FRR are analyzed. The system was tested with 400 test signature samples, which include genuine and forgery signatures of twenty individuals. With this system, a false rejection ratio of less than 0.1 and a false acceptance ratio of less than 0.2 are achieved.