Biological dendritic cells perform a complex activation/suppression role in the generation, direction, and control of antibodies. Their action relies on balancing information regarding the external antigen type, amount, and virulence, as well as the state and resources of the host organism. In this paper, an information processing algorithm inspired by the functionality of the dendritic cells is proposed to enhance aircraft subsystem abnormal condition detection and identification within the artificial immune system paradigm. A hierarchical multi-self-strategy is used to produce multiple failure detection and identification outcomes at each sample time over a time window. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets this information to decide upon a unique detection and identification outcome with reduced false alarms and a low number of incorrect identifications. A mathematical formulation of the concept and a detailed implementation algorithm are provided. The proposed methodology is demonstrated using simulation data for a supersonic fighter from a motion-based flight simulator at nominal conditions, under failures of actuators, malfunction of sensors, and wing damage. In all cases considered, the detection and identification scheme achieves excellent detection and identification rates with practically no false alarms.