Fault diagnosis and failure prognosis of critical dynamic systems, such as aircraft and industrial processes, rely on degradation or fatigue models and measurements typically acquired on-line in real-time. Such sensor data must be preprocessed in order to remove artifacts and improve the signal-to-noise ratio. Furthermore, they must be processed appropriately so that useful information in compact form can be extracted and used to detect incipient failures and predict the remaining useful life of failing components. We present a methodology to select an optimum feature vector from a list of candidate features, prioritize and rank them to meet set performance objectives. The enabling technologies include genetic programming tools, data fusion and model-based approaches for feature selection and extraction. We will suggest a multi-core processing environment for the efficient and expedient implementation of these technologies. Performance metrics are defined to assess the efficacy of the methodology. Typical examples from aircraft systems are used to demonstrate the proposed techniques.