The work presented in this paper will highlight selected artificial intelligence approaches as applied within an Integrated Vehicle Health Management (IVHM) system. The selected vehicle subsystem areas to be discussed include electro-mechanical actuators (EMAs), propulsion system performance, vehicle structural integrity and general signal anomaly detection. Artificial intelligence methods including neural networks, fuzzy logic and trained probabilistic classifiers are described within the context of the selected subsystem applications. In addition, discussion on individual subsystem health condition indicators as applied within an intelligent, model-based reasoning approach is presented that examines health state and functional availability of individual components, subsystems, and the overall vehicle. The AI implementations described herein illustrate the integration of detection, diagnostic, and prognostic reasoning capabilities from across critical subsystems on a vehicle platform. The examples provided illustrate how the selected AI technologies can be implemented throughout an end-to-end application, from data signal quality checks to off-board prognostic assessments.