In this paper, position control of a car-like mobile robot using a neural network is presented. positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as a reference, the robot posture is corrected by a cascaded controller. To improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The neural network functions as a compensator to minimize the positional errors in on-line fashion. A car-like mobile robot is built as a test-bed and experimental studies of several controllers are conducted and compared. Experimental results show that the best position control performance can be achieved by a cascaded controller with a neural network.