Cognitive computing refers to an emerging family of problem-solving methods that mimic the intelligence found in nature. The common goal of these methods is to crack tough problems that have resisted straightforward analytic solutions, such as intractable problems caused by combinatorial explosions. This paper describes the application of a combination of three of these methods, fuzzy logic, artificial neural networks, and genetic algorithms in a unique manner to provide a solution to rapidly develop flight control systems for unmanned aircraft. The environment resulting from the combination of these three methods has been successfully applied or is currently being applied to the flight control system development for four unmanned rotorcraft: a full scale Bell Helicopter UH-1H aerial target, an American Sportcopter Ultrasport 254 single sear ultralight helicopter, a custom developed 45 pound miniature helicopter operated by the Army at NASA Langley Research Center, and an electronic countermeasures decoy developed at the Naval Research Laboratory. Additional investigations have begun using this approach for the development of flight control system for fixed wing aircraft as either an autopilot for manned flight or as a controller for an unmanned vehicle. This paper gives a broad overview and technical description of these projects.