The management of risk associated with products and processes is an important consideration that must be not be overlooked Inherent risk exists in the development, manufacture, launch, and usage life-cycle for any product or process All types of products and processes carry risk should the product or process fail to produce the intended results within the required time frame, the risk management activities and techniques that are currently applied to products and processes focus primarily at the domain level. Domain level failure analysis provides an opportunity to mitigate risk of the product or process itself. However, there exists a need fox a higher-level approach to managing risk early within the life-cycle of a product or process Furthermore, risk management should be supported by a generic system representation and accompanying algorithm rather than relying upon human engineers to develop a level of confidence that a complete set of risks has been identified and addressed. This work proposes the creation of a System Knowledge Representation, a set of meta-level rules, and a meta-level failure analysis algorithm that is processed using CLIPS expert system software to identify areas of potential system risk The theoretical result is a more robust approach to system analysis and an ability to identify and manage risk of products and processes at multiple levels of abstraction. From a practical perspective, the Meta-Level Failure Algorithm (MLFA) is a facilitator of the core information toward enabling automatic Failure Modes and Effects Analysis (FMEA) generation as a pre-cursor to human analysis. This research is directed toward and validated against automotive electromechanical systems.