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A large rule-based expert system with each rule involving perhaps 10 out of l00,000 possible Boolean criteria, can require a significant amount of processing time to evaluate. This time can be reduced if all rules have a single consequent and have antecedents that contain only conjunctions of the Boolean criteria or their complements. If the consequences do not insert new facts into the rule-base, then parallel processing can be used with great efficiency. The value of a rule-based expert system to help solve a variety of diagnostic and advisory needs has been well-demonstrated over the last 2 decades. Parallel processing has become increasingly important for embedded systems in order to accelerate a variety of computations. This report discusses research connected to the development of a data structure and algorithm to perform parallel inferencing in rule-based systems. It also discusses a simulation technique for estimating the number of processors needed to evaluate a given number of rules and criteria within the required time. Expert systems, Knowledge-based systems, Rule-based Systems, Artificial intelligence, Real-time Processing, Parallel processing, Distributed computing, Decision support, Cockpit Automation, Function allocation, Simulation.