Low-level navigation for autonomous vehicles can be accomplished efficiently by a behavioral-based approach that involves the simultaneous execution of independent sub-tasks seen as primitive behaviors. Each behavior maps sensory data into control commands in a reactive way, with no need of internal representations. A useful tool for realizing such a direct mapping is fuzzy logic, that allows the production of control rules by either manual programming or automatic learning. In prospect of implementing an articulated control system including all the low-level behaviors of navigation, this paper focuses on the problem of obtaining an efficient and robust fuzzy controller performing a single behavior and presents a method for minimizing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side. Fuzzy rules, that map ultrasonic sensor readings onto steering velocity values, are learned automatically from training data collected during operator-driven runs of the vehicle. In addition, we address the problem of defining an appropriate performance function, that may be useful for evaluating the influence of the rule base reduction on the overall behavior of the vehicle during navigation, but also for estimating the quality of a control rule, in order to adapt rules on- line. Results of an experimental comparison between the original fuzzy wall-follower and its optimized version are reported.