A detection scheme based on the artificial immune system (AIS) paradigm was developed for specific classes of aircraft jet engine failures including throttle, burner fuel flow valve, variable nozzle area actuator, variable mixer area actuator, low-pressure spool speed sensor, low-pressure turbine exit static pressure sensor, and mixer pressure ratio sensor. The Modular Aero-Propulsion System Simulation model developed by NASA has been linearized and interfaced with a supersonic fighter aircraft model and a motion-based flight simulator to provide the adequate framework for the development and testing of the detection scheme. A variety of aircraft engine actuator and sensor failures were modeled and implemented into this simulation environment. A 5-dimensional feature hyper-space was determined to build the "self" within the AIS paradigm for abnormal condition detection purposes. The AIS interactive design environment based on evolutionary algorithms developed at West Virginia University (WVU) was used for data processing, detector generation, and optimization. Flight simulation data for system development and testing were acquired through experiments in the WVU 6 degrees-of-freedom flight simulator over extended areas of the flight envelope. The AIS-based detection scheme was tested using both nominal and engine failure conditions and its performance evaluated in terms of detection rates and false alarms. Results show that the AIS-based approach has excellent potential for the detection of all the classes of engine failures considered.