This paper proposes an intelligent controller for an air-handling unit to control the temperature while limiting the humidity below 70%. The proposed scheme is based on the back-propagation-through-time approach. It uses artificial neural networks to develop an emulator to learn on-line the plant dynamics and a controller to control the fan speed and chilled water valve opening in real time. The neural-based controller was implemented on an industrial air handler for performance validation purposes. The results show that the intelligent controller could effectively control the temperature and humidity within the operating range investigated. Those intelligent controllers could be practical alternatives for controlling nonlinear and complex air-conditioning systems.