Identification of ignitable liquid residues in the presence of background interferences, especially those arising from pyrolysis processes, is a major challenge for the fire debris analyst. This research examined a mathematical model that allows for the detection of an ignitable liquid in a fire debris sample and the classification of the ignitable liquid. This project proposed to investigate the development of a method for classifying fire debris GC-MS data sets as: 1) containing or not containing an ignitable liquid, 2) classifying any ignitable liquid that may be present, and 3) estimating the statistical certainty of the answers to questions 1 and 2. The proposed approach was to build a mathematical model that can correctly classify data from ignitable liquids and pyrolyzed substrates (wood, plastic, etc.). The model would then be applied to data from laboratory-generated fire debris samples, as well as ignitable liquids and substrates that were not used to build the model. The classification success of the model would allow a determination of the statistical performance of the model. The model would be developed based on the total ion spectrum, which has already shown a propensity for classifying a set of ignitable liquids drawn from multiple ASTM classes.