Many different types of features can appear on the surface of wooden boards, lineals or parts. Some of these features should not appear on the surfaces of wood products. These features then become undesirable or removable defects for those products. To manufacture these products boards are cutup in such a way that these undesirable defects will not appear in the final product. Studies have shown that manual cutup of boards does not produce the highest possible yield of final product from rough lumber. Because of this fact a good deal of research work has been done to develop automatic defect detection systems. Color images contain a lot of valuable information which can be used to locate and identify features in wood. This is evidenced by the fact that the human color vision system can accurately locate and identify these features. A very important part of any automatic defect detection system based wholly or impart on color imagery is the location of areas that might contain a wood feature, a feature that depending on the product being manufactured may or may not be a defect. This location process is called image segmentation. While a number of automatic defect detection systems have been proposed that employ color imagery, none of these systems use color imagery to do the segmentation. Rather these systems typically average the red, green, and blue color channels together to form a black and white image. The segmentation operation is then performed on the black and white image. The basic hypothesis of this research is that the use of full color imagery to locate defects will yield better segmentation results than can be obtained when only black and white imagery is used. To approach the color wood image segmentation problem, two conventional clustering procedures were selected for examination. Experiments that were performed clearly showed that these procedures, ones that are similar in flavor to other unsupervised clustering methods, are unsuitable for wood color image segmentation. Based on the experience that was gained in examining the unsupervised clustering procedures, a model based approach is developed. This approach is based on the assumption that the distribution of colors in clear wood is Gaussian. Since boards that are used by the forest products secondary manufacturing industry are all such that most of their surface area is clear wood, the idea is to use the most frequently occurring colors, i.e., the ones that must represent the most likely colors of clear wood, to estimate the mean and covariance of the Normal density function specifying the possible colors of clear wood. Deviations from this model in the observed histogram are used to identify colors that must be caused by features other than clear wood that appear on the surface of the board. / Master of Science