In this SBIR project, a reliable self-repairing Flight Control System (FCS) will be developed. To achieve this goal, an artificial Neural Network based Sensor Validity Monitoring, Verification and Accommodation (SVMVA) scheme will be developed. Initially, the Extended Back Propagation Algorithm (EBPA) or Learning Vector Quantization (LVQ) will be evaluated and employed as the on-line real time learning, monitoring and estimating tools. For a feasibility study, sample flight vehicle dynamics will be assessed. Then, an optimal on-line estimator for the flight dynamics will be shown by applying our unique input vector discrimination procedure and network optimization technique. In Phase I, an in-house numerical simulation tool for SVMVA will be developed to demonstrate the feasibility of our approach. As a result, it is expected that the new self-repairing Flight Control System based on SVMVA scheme will meet or exceed the NASA's requirement for their new flight vehicles. The most significant advantage of the new self-repairing Flight Control System is that the system is able to mask failed physical sensors by providing flight-worthy flight dynamics information to most FCS of flight vehicles without needing redundant sensors or excessive power, weight and space.