Scaling factor tuning is one of the most used method to enhance the performance of a fuzzy controller. This paper presents two intelligent tuning strategies to tune this factor. In the first strategy, a supervisor fuzzy controller SFC was designed to continuously adjust, on line, the scaling factor of the basic fuzzy controller BFC based on the error and change of error signals. In the second strategy, a neural network NN is used to do this task. Performance of the tuning strategies are compared with corresponding conventional fuzzy controller in terms of several performance measures such as steady state error, settling time, rising time, and peak overshoot. Simulation results show that SFC performance is better. The system implementation and tests are carried out using LabVIEW (V 8.2).