Abstract: A preprocessor translates problem descriptions in text format to a numeric form, usable by a neural network. Problem descriptions and associated solutions are used as training sets for the neural network. From a word domain collected from a number of problem descriptions, each character used in the problem domain is assigned a character frequency value. These frequency values are used to translate each problem description to an input vector, where each input value represents a character and each input value is associated with an input node of the neural network. In addition to being associated with a frequency value, each character is scaled and normalized to improve accuracy of the neural network's recall capabilities.