This paper reports the results of the classifications algorithms used, in data mining, for the creation of a navigation model applied to an autonomous robot to perform the task of wall-following. The goal is to make the robotic vehicle capable of moving, guiding and making decisions autonomously, avoiding collisions, using the smallest resources set as possible. A simulator was used to develop a test environment, the robot vehicle and simulate the proposed navigation system. The results suggest that the J48 (C4.5) classification algorithm obtained the most satisfactory results, generating a model with high degree confidence in decision making.