The automatic wood recognition is studied in this paper through wood stereogram for its convenient way to obtain. Firstly, a standardizing preprocess of wood images was carried out. Secondly, block local binary pattern (LBP) was selected to extract wood features and three different distances (European distance, Chi-square distance, and diffusion distance) were introduced to classification experiments. At last, we used nearest neighbor classifier to identify wood features, discussed effects of block LBP features on recognition results and compared recognition rates in different distances. Results show that different block ways have significant influence on the final classification, among which block along the tree ring direction shows downward trend and proper block will improve the recognition rates in the vertical direction of tree ring. Chi-square distance can obtain the best recognition rate, up to 93.3%, 2.5% higher than that of European distance.