Conventional method of assessing the performance of gas turbine engine involvesrnanalyzing different engine parameters manually and comparing them with theirrnrespective acceptable limits in engines maintenance manuals . The method takesrnlengthy and complicated processes that demand personnel with many years ofrnexperience and allows subjective judgment of the personnel involved in evaluationrnprocess. Technological advances in design and constructions of gas turbine enginesrnadds more eng in e parameters thereby setting more hurdles on the process ofrnengines performance evaluation .rnThis paper reports on the finding of a research that had the objective to build arnmodel that classify the performance, either accepting or rejecting, of PT6A-27 modelrnturboprop pas turbine engine . The engines cons id erred were those undergo ingrnevaluation for performance after they went through repair or overhaul.rnThe data used to build the mode first passed through different data preprocessingrnand analyzing techniques . the model employed neural network built usingrnback propagation algorithm on a neural network tool box found in MATLAB 6.5 .rnThe model built classifies gas turbine engines by their performances into theirrnrespective classes . The classification accuracy found was encouraging for the modelrnto be adapted in real problem. The outcome of this research can also put a cornerrnstone for further researches in using data mining technique for gas turbine enginernmaintenance .