In this paper, we realize the classification of the gray cast iron according to the graphite morphology in it by Artificial Neural Network. It's a part of a big metallurgic analytical software system, and also takes on some significance in the automatic production in iron and steel industry. Our work is described as 2 steps here: The first one is texture feature extracting and the second one, classification. The images we worked on come from metallographic electron microscope, and in needs, we do some pretreatment on it. The textural features extracted mainly based on fractal parameter, roughness parameter and regression, and some comparison is also made between these textural modes. The classification is performed through artificial neural network--multilayer back-propagation neural network, which is based on a kind of feed-forward artificial neural network. It learns samples and trains itself by BP algorithm--error back propagation algorithm. To reduce the computational quantity, we obtain the number of hidden nodes directly by the numbers of input nodes and output nodes. Result shows available.