This thesis describes the design of an image interpretation system for the automatic detection of internal hardwood log defects. The goal of the research is that such a system should not only be able to identify and locate internal defects of hardwood logs using computed tomography (CT) imagery, but also should be able to accommodate more than one type of wood, and should show potential for real-time industrial implementation. This thesis describes a new image classification system that utilizes a feed forward artificial neural network as the image classifier. The classifier was trained with back-propagation, using training samples collected from two different types of hardwood logs, red oak and water oak. Pre-processing and post-processing are performed to increase the system classification performance, and to make the system be able to accommodate more than one wood type. It is shown in this thesis that such a neural-net based approach can yield a high classification accuracy, and it shows a high potential for parallelism. Several possible design alternatives and comparisons are also addressed in the thesis. The final image interpretation system has been successfully tested, exhibiting a classification accuracy of 95% with test images from four hardwood logs.