Complex dynamical systems, such as aircraft, chemical processes, power plants, shipboard equipment, etc., are required to maintain an acceptable level of operational integrity and availability. Current research aims to maximize uptime by maintaining such systems only when required. A viable and cost-effective diagnostic/prognostic system architecture must integrate a number of functionalities while exhibiting attributes of flexibility and scalability. It must account for fault modes that are inherent to the current operating state of the system and its usage patterns (Hadden et al. 1999). Furthermore, it must be able to predict accurately the remaining useful lifetime of failing components and manage effectively uncertainty (Hadden et al. 1999). This paper introduces an integrated diagnostic/prognostic architecture that builds upon means to identify the system’s operating mode and usage pattern using concepts from hybrid system theory and Petri networks as decision support tools, mechanisms to extract an optimum feature vector based on data-mining and diagnostic/prognostic algorithms that are designed employing a fuzzy logic expert system paradigm and static/dynamic wavelet neural network constructs for fault detection/isolation and for estimation of the remaining useful lifetime of a failing component. Essential elements of the architecture are implemented and validated on a laboratory scale process consisting of multiple tanks, control equipment, sensors and actuators.