Temperature and humidity are two highly coupled variables in a control system, which need to be decoupled for effective control. Moreover, the coupling problem may get more severe and the two control loops may produce a strong interference to each other that can cause system instability when the humidity is measured by dry-and-wet bulb method. In this study, a control method based on fuzzy-neural-network was studied for solving the coupling problem. The shape of membership function can be adjusted in time by using a wavelet basis as the fuzzy membership function. An effective real-time decoupling control system for temperature and humidity could be realized by neural network fuzzy inference. Decoupling control tests were conducted in a control room with 1.6 m × 1.0 m × 4.0 m. The results show that the performance of the control system on dynamic response speed, stability, and anti-jamming have been improved after decoupling.