This paper describes the design, development, and flight-simulation testing of an artificial-immune-system-based approach for the evaluation of different aircraft subsystem failures/damages. The evaluation consists of the estimation of the magnitude/severity of the failure and the prediction of the achievable states, leading to an overall assessment of the effects of the failure on reducing the flight envelope. A supersonic fighter model is used, which includes model-following adaptive control laws based on nonlinear dynamic inversion and artificial neural network augmentation. Data collected from a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the proposed approach. Example results are presented for failure-magnitude evaluation and flight-envelope-reduction prediction for abnormal conditions affecting sensors, actuators, engine, and wing structure. Successful failure detection and identification are assumed before evaluation. The results show the capabilities of the artificial-immune-system-based scheme to evaluate the severity of the failure and to predict the reduction of the flight envelope in a general manner.