Many images to be processed are textured images such as medical images, landscape images, and mineral images. Textured images are classified and segmented using 2-D autoregress (AR) model and neural network. The network consists of three subnets: input subnet, analysis subnet, and classification subnet. The network is used to both establish a 2-D AR model for a texture region and to implement region identification and segmentation. Good performance is achieved by an adaptive learning process which accurately estimates an AR model for a texture. After the learning, a frame from textured image is input and forwarded directly through the neural network, the computation time for texture feature extraction is dramatically reduced.