Abstract: The writer identification of text-independent is a subject in the area of image processing and pattern recognition,which has a wide range of potential applications.A feed-forward neural network method is proposed and applied to writer identification.The two problems that feature extraction from gray image and design of feed-forward neural network are discussed.Eighteen gray features of three sorts are extracted from a person's handwriting image.And a new genetic algorithm is proposed to optimize the structure and connection weight for feed-forward neural networks.Experiments have been conducted for writer identification with 18 gray features of per handwriting image from 10 persons.The result indicates the new genetic algorithm can optimize structure and connection weight of feed-forward neural networks together,converge to the global optimum quickly,hardly get stuck at local optimum,and increase the true rate efficiently.