This paper presents the development and application of an integrated artificial-immune-system-based scheme for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. The proposed approach is based on a hierarchical multiself strategy in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a subregion of the flight envelope. The aircraft model represents a supersonic fighter, including model-following adaptive control laws based on nonlinear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all four categories of failures considered.