 results are utilized in an analysis technique such as Markov process modeling to make assertions regarding the availability characteristics of the system. Expert Systems, Fuzzy Logic, and Neural-Net Techniques [Loos94] [Velazco95] [Velazco96][Cheynet98] The capability of expert systems, fuzzy logic, and neural-net processors has been investigated for the benefits which they might provide to fault tolerant systems and specifically to the operation of space systems. Expert systems have been used as the basis for fault tolerance provisions, utilizing recovery strategies which are selected based upon a probabilistic understanding of the options and alternatives available to the processor. Although cursed with a very whimsical title and basic elements (fuzzy-sets, -operators, -inference, and fuzzy-rules), fuzzy logic is built upon the use of multi-valued logic to define levels of a true or false assertion rather than an absolute one or zero. Fault injection experiments on a system implemented using the Weight Associative Rule Processor (WARP) showed that the majority of upsets resulted in either no effect or minor control disturbances; few resulted in loss of control function or crash of the system. An Artificial Neural Network (ANN) is a computing device inspired by observing the operation of the brain's biological neuron. It consists of a number of simple processing elements, connected with variable weights or strengths, capable of operating in a highly parallel fashion. Fault injection experiments conducted to simulate the effects which single event upsets would produce in the memory of artificial neural net processors indicate that between 86% and 99% of injected memory errors did not provoke output errors in two ANN implementations. Fuzzy logic and neural networks potentially complement each other in their ability to provide solution to fault tolerance requirements. At the present time the commercialization of fuzzy logic and artificial neural net processor implementations has not produced high performance, aggr