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A computer vision-based lumber production planning system (CVLPPS) is described. Computer axial tomography (CT or CAT) images of hardwood logs are analyzed for identification and classification of internal log defects. Individual CT image slices are analyzed for detection of 2D defects which are correlated across CT image slices in order to establish 3D support and identify true 3D defects. CVLPPS is capable of 3D reconstruction and rendering of the log and its internal defects from the individual CT image slices. It is also capable of simulation and rendering of key machining operations such as sawing and veneering on the 3D reconstructions of the logs. From the 3D reconstruction of the log and knowledge of its internal defects, CVLPPS can formulate sawing strategies to optimize the yield and grade of the resulting lumber. A prototype CVLPPS was developed and tested on CT images of hardwood logs from White Ash, Hard Maple, Red Oak and Black Walnut. Experimental results showed that CVLPPS could identify and localize a large majority of the internal log defects and thereby result in a 23–63% gain in value yield recovery when compared to a lumber processing strategy that did not use internal log structure information. Issues pertaining to the deployment of CVLPPS in a real-time lumber production environment are discussed. It is shown that the various components of CVLPPS are amenable to parallel and distributed computing, thus making a real-time implementation of CVLPPS practically feasible. CVLPPS could also be used as a decision aid for lumber production planning and an interactive training tool for novice sawyers and machinists.