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The fault-tolerance characteristics of time-continuous, recurrent artificial neural networks (ANNs) that can be used to solve optimization problems are investigated. The performance of these networks is illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to up to 13 simultaneous stuck-at-1 or stuck-at-0 faults for network sizes of up to 900 neurons. The effect of these faults on the performance is demonstrated, and the cause for the observed fault-tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations and the potential benefits of delegating a critical task to a fault-tolerant network are discussed