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This project was completed to determine if fuzzy set classification could be used to accurately evaluate data collected during a psychophysiological detection of deception examination. This methodology provides an alternative to the proprietary statistical technique now commonly used. Data collected using both the Modified General Question Technique (MGQT) and the Relevant Only formats were evaluated. An extensive and, arguably, complete set of polygraph data features was identified. These polygraph data features were not individual dependent, examiner dependent, or in any way dependent on apriori or posteriori knowledge (statistics) of the data. A fuzzy K-Nearest Neighbor classifier and an adaptive fuzzy Least Mean Squares classifier were developed. A fuzzy C-Means clustering algorithm which enabled visualization of the data features was also developed. The fuzzy algorithms were 'forced' to make a choice of truth versus deception; they could, however, be used to return a number that would, in near real-time, give the examiner an idea of the confidence level of the algorithm. The data were parsed such that 25% of the data were tested using an algorithm developed from the remaining 75% of the data. It is shown that only four features are needed to achieve 100% correct classification of the Relevant Only data and 97% correct classification of the MGQT data. It is suggested that any future research development, or testing or computer classification techniques, including statistical and neural techniques include the results of this work.