In the centralized heating, ventilating and air-conditioning (HVAC) system, air handling units (AHU) are traditionally controlled by single loop proportional-integral-derivative (PID) controllers. The control structure is simple, but the performance is usually not satisfactory. In this paper, we propose a cascade control strategy for temperature control of AHU. Instead of a fixed PID controller, a neural network controller is used in the outer control loop. This approach not only avoids the tedious tuning procedure for the inner and outer loop PID parameters of a conventional cascade control system, but also makes the whole control system be adaptive and robust. The multilayer neural network is trained online by a special training algorithm simultaneous perturbation stochastic approximation (SPSA) based training algorithm. With the SPSA based training algorithm, the weight convergence of the neural network and stability of the control system is guaranteed. The novel cascade control system has been implemented to improve supply air temperature control performance of AHU in a pilot HVAC system. The experimental results demonstrate the effectiveness of proposed algorithm over classical control systems.