This paper presents a new visual inspection method to classify wooden plates used in pencil manufacturing. Wooden plates with darker regions possibly have growth rings. Pencils manufactured with these plates are more difficult to sharpen and have a tendency to bend and crack, therefore these plates are classified as not adequate for pencil manufacturing. The proposed method is based on the extraction and analysis of the features of the wooden plates using gray level images. The method classifies the plates using the results obtained by an automatic threshold determination based in Shannon's entropy. The method was idealized aiming low computational complexity, i.e., the algorithm calculations involving only simple operations such as addition, subtraction, multiplication and division which could be implemented using VLSI technology. The wooden plate is mapped in an optimal number of regions. Each region is pre-classified considering as relevant features the total entropy, the total entropy curve asymmetry, the threshold level found for the region, the ratio between the entropy of the shapes and the background entropy and also the deviation between the shapes' maximum entropy point and the background's maximum entropy point. All the region information are combined based in heuristic decision rules, arriving in a pre-classification stage where the regions are labeled in four classes (A, B, C and X), letter A represents the best class. Two decision algorithms have been investigated for the final classification: the first one is based on a co-occurrence matrix considering only unite- directional horizontal neighborhood of the regions and the second one based in a heuristic method of information reduction considering combinations of the pre-classified regions. The final results obtained by the two algorithms are compared with the classification made by a human expert, demonstrating that the proposed method had a very good performance.