This paper deals with texture segmentation in images of wooden plates. These plates are used in pencil industry and its quality reflects on the quality of the industrialized product. We have used the spatial gray level dependence method for analyzing the plate surface, detecting nodes, stripes, and defects. This statistical method has many applications in several fields with textures. We classify the pates based on the industry requirements. With images of 128 X 256 pixels and 256 gray levels, we have used a 64 X 64 window placed over 8 non- overlapping areas. For each position the window computes the co-occurrence matrices for the directions 0, 45, 90 and 135 degrees with a chessboard distance equal to 1. The values of he angular second moment average, computed in the four directions versus the plate area, are plotted in a feature space. This shows clusters of similar textures around constant values. The good plates have a homogeneous visual appearance. The unusable plates have nodes, stripes and defects. Between then exist the high stripped plates and the medium stripped plates. We can classify then in good one or unusable depending on the industry demand. The method shows a good efficiency for discriminating between textures of the same material but with a random distribution established by the nature.