We introduce an immersive system prototype that integrates face, gesture and speech recognition techniques to support multi-modal human-computer interaction capability. Embedded in an indoor room setting, a multi-camera system is developed to monitor the user facial behavior, body gesture and spatial location in the room. A server that fuses different sensor inputs in a time-sensitive manner so that our system knows who is doing what at where in real-time. When correlating with speech input, the system can better understand the user intention for interaction purpose. We evaluate the performance of core recognition techniques on both benchmark and selfcollected datasets and demonstrate the benefit of the system in various use cases.