In order for an industrial inspection system to be of utility in manufacturing it must be fast, accurate, and flexible [Chin 1986]. Current machine vision systems are very specialized and inflexible in nature. A reason for the inflexibility of current machine vision systems is the need for real-time processing of image data. Such a need has forced both the use of very specialized image processing hardware as well as the use of rather simple, very specialized computer vision algorithms to do the analysis. On the other hand, most, if not all, of today’s computer vision methods are not general purpose in nature. In the absence of truly robust general purpose methods, developing satisfactory machine vision solutions will continue to involve experimenting with machine vision hardware and software components. Given the current state of machine vision technology, it would seem that the best method for creating flexible machine vision systems is, perhaps, to define a subclass of inspection problems where all the problems within the subclass have a number of common features about them. Such a subclass must be of interest to a number of manufacturers. It must also be “reasonable” to solve, given the current state of the art. Once the subclass has been selected, the next logical step would seem to be to create a device that makes performing all the needed experiments on the various problems within the class easy to perform. Based on the above line of reasoning, this work has four major objectives. The first objective is to define a meaningful subclass of inspection problems that are a) of interest to a number of manufacturers, and b) represent inspection tasks that seem “reasonable” within the current state-of-the-art of computer vision. The subclass of inspection problems selected for this work is the longitudinal planar web inspection problem under the two-dimensional imaging restriction. The second objective of this work is to create a vehicle that will allow the types of experimentation usually associated with the development of machine vision systems to be facilitated. This vehicle created is called a “machine vision prototyper.” The third objective of this work is to use the machine vision prototyper system to attack a particular planar web applications problem. The application considered is the problem of locating and identifying surface defects in surfaced hardwood lumber in a species independent manner. The fourth objective of this research is to indicate how the prototyper system can be used to attack a second planar web application problem. This application problem is the inspection of hardwood parts coming out of a molder. The utility of the machine vision prototyper system as an experimental tool is demonstrated on two of the three possible types of longitudinal planar web inspection problems. The results include the development of a machine vision system for a hardwood surfaced lumber surface feature detection problem, and a discussion of how the prototyper can be used to attack the problem of inspecting hardwood parts coming out of a molder. / Ph. D.