It is critical to maintain jet fighter aircraft so that they are in the best flying condition. Among the various components of aircraft, the environmental control system is crucial as it provides cool air for the cabin and electronic equipment. In this study, we develop a model that predicts the remaining working hours of the critical components of aircraft using a back-propagation neural network (BPNN). First, we adopted the Delphi method with repeated questionnaire surveys to select six key components in the environmental control system, where the cooling turbine was considered the most important component for the prediction model. Then, with the same method, four of the key parameters for the cooling turbine were selected to establish the prediction model. Actual maintenance data from 2009 to 2013 were used for training the neural network to produce the smallest errors between the actual working hours according to inspection results and the predicted working hours. This verification result showed that the correlation and the goodness of fit were above 0.984 and 0.963, respectively, and the prediction accuracy was 93%. These results indicate that the prediction model with the BPNN can be effectively used to predict the remaining working hours of the cooling turbine. The model can reduce the cost of maintenance and the time lost from the disassembly of components and the halting of flights, as well as unnecessary wear and tear.