Biometrics is refers to the automatic identification of a living person based on physiological or behavioral characteristics. Hand identification involves an analysis and measures of the features of the hand. In this research work, we have two steps. The first step is enrollment step and the second is identification step. In the enrollment step, the stages image capture, image binarization, edge detection and feature extraction were implemented. In the image capture, the user has to put his hand in the scanner with fingers spread freely without using any pegs. In the image binarization the color image is converted to black and white image. And in the edge detection the Laplace operator was used to find the hand boundary. In the feature extraction two types of features (geometrical and nongeometrical features) were extracted. The geometrical features are fingers length, finger width, hand span and distance between joints. The central moment to each finger after finding the fingers direction were extracted as nongeometrical features. In the identification step, the feature vector to the unknown person is extracted from its hand image. Two methods for identify the feature vector of the unknown person with those listed in the database for 13 persons; where for each person 5 images are taken as training samples. The first adopted method is fuzzy method with difference membership function (i.e., a triangular, trapezoidal and bell shape function) and the second method is a fuzzy-neural method (fuzzy self organization map). By using any one of the above methods we can identify the feature vector of the unknown person. By the test it is shown that the trapezoidal membership function shows better performance in comparison with the others.