This paper describes a method of acquiring behaviorist-based reactive control strategies for an autonomous skid-steer robot operating in an unknown environment. First, a detailed interactive simulation of the robot (including simplified vehicle kinematics, sensors and a randomly generated environment) is developed with the capability of a human driver supplying all control actions. We then introduce a new modular, neural-fuzzy system called Threshold Fuzzy Systems (TFS). A TFS has two unique features that distinguish it from traditional fuzzy logic and neural network systems; (1) the rulebase of a TFS contains only single antecedent, single consequence rules, called a Behaviorist Fuzzy Rulebase (BFR) and (2) a highly structured adaptive node network, called a Rule Dominance Network (RDN), is added to the fuzzy logic inference engine. Each rule in the BFR is a direct mapping of an input sensor to a system output. Connection nodes in the RDN occur when rules in the BFR are conflicting. The nodes of the RDN contain functions that are used to suppress the output of other conflicting rules in the BFR. Supervised training, using error backpropagation, is used to find the optimal parameters of the dominance functions. The usefulness of the TFS approach becomes evident when examining an autonomous vehicle system (AVS). In this paper, a TFS controller is developed for a skid-steer AVS. Several hundred simulations are conducted and results for the AVS with a traditional fuzzy controller and with a TFS controller are compared.