This thesis describes the development of a software system for color sorting hardwood edge-glued panel parts. Conceptually, this system can be broken down into three separate processing steps. The first step is to segment color images of each of the two part faces into background and part. The second step involves extracting color information from each region labeled part and using this information to classify each part face as one of a pre-selected number of color classes plus an out class. The third step involves using the two face labels and some distance information to determine which part face is the better to use in the face of an edge-glued panel. Since a part face is illuminated while the background is not, the segmentation into background and part can be done using very simple computational methods. The color classification component of this system is based on the Trichromatic Color Theory. It uses an estimate of a part's 3-dimension (3-D) color probability function, P, to characterize the surface color of the part. Each color class is also represented by an estimate of the 3-D color probability function that describes the permissible distribution of colors within this color class. Let P_omega_i denote the estimated probability function for color class omega_i. Classification is accomplished by finding the color difference between the estimated color probability function for the part and each of the estimated 3-D color probability functions that represent the color classes. The distance function used is the sum of the absolute values of the differences between the elements of the estimated probability function for a class and the estimated probability function of the part. The sample is given the label of the color class to which it is closest if this distance is less than some class specific threshold for that class. If the distance to the class to which the part is closest is larger than the threshold for that class, the part is called an out. This supervised classification procedure first requires one to select training samples from each of the color classes to be considered. These training samples are used to generate P_omega_i for each color class omega_i and to establish the value of the threshold T_i that is used to determine when a part is an out. To aid in determining which part face is better to use in making a panel, the system allows one to prioritize the various color classes so that one or more color classes can have the same priority. Using these priorities, labels for each of the part faces, and the distance from each of the part faces' estimated probability functions to the estimated probability function of the class to which each face was assigned, the decision logic selects which is the ``better'' face. If the two part faces are assigned to color classes that have different priorities, the part face assigned to the color class with higher priority is chosen as the better face. If the two part faces have been assigned to the same color class or to two different classes having the same priority, the part face that is closest to the estimated probability function of the color class to which it has been assigned is chosen to be the better face. Finally, if both faces are labeled out, the part becomes an out part. This software system has been implemented on a prototype machine vision system that has undergone several months of in-plant testing. To date the system has only been tested on one type of material, southern red oak, with which it has proven itself capable of significantly out performing humans in creating high-quality edge-glued panels. Since southern red oak has significantly more color variation than any other hardwood type or species, it is believed that this system will work very well on any hardwood material.