Knowledge of internal log defects, obtained by scanning, is critical to efficiency improvements for future hardwood sawmills. Nevertheless, before computed tomography (CT) scanning can be applied in industrial operations, we need to automatically interpret scan information so that it can provide the saw operator with the information necessary to make proper sawing decisions. Our current approach to automatically label features in CT images of hardwood logs classifies each pixel individually using a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this ANN was able to classify clearwood, bark, decay, knots, and voids in CT images of two species of oak with 95% pixel-wise accuracy. Recently we have investigated other ANN classifiers, comparing 2D versus 3D neighborhoods and species-dependent (single species) versus species- independent (multiple species) classifiers using oak, yellow poplar, and cherry CT images. When considered individually, the resulting species-dependent classifiers yield similar levels of accuracy (96 - 98%). 3D neighborhoods work better for multiple-species classifiers and 2D is better for single-species. Under certain conditions there is no statistical difference in accuracy between single- and multiple-species classifiers, suggesting that a multiple- species classifier can be applied broadly with high accuracy.