If a human can perform an image processing task then, given sufficient time and determination an expert can often develop a machine vision system to emulate this performance. The work reported here is part of a long-term project that is aimed at the development of software systems that can learn by example and emulate human performance. The non-algorithmic approach of using a neural network based window filter (NNWF) has been used. General access is provided to the results of this research via Adobe Plugin Protocol functions available for use in image processing packages. This paper reports the use of task specific knowledge to initialise the network weights prior to training. Supervised neural network training is a high dimensional optimisation problem and the initial conditions of the search are critical to the quality of the solutions found (local or global optima) and the speed of convergence to the solution. These initial conditions should be problem specific but standard training methods, backpropagation, Levin-Marquadt etc. start with the initial weights set to small, random numbers. The weight set of a trained network embodies knowledge of the task; it is in some sense a description of the task. The initial weight set is, by this reasoning, a partial description of the task and could be derived from the available problem knowledge. It is often possible to partially describe an image processing task as a set of rules. These rules may be fuzzy in nature, incomplete and ambiguous but still provide a useful guide for a human attempting the task. A mapping scheme has been developed that can be used to map simple Boolean rules to a fuzzy neural network whose architecture reflects the structure of IF THEN rules. The fuzzy neural network (FuNN) architecture is described.