In this paper, a real time physiological signal classification system with an integrated ultra-low power collaborative neural network classifier is presented. The developed system includes a specially designed system-on-chip (SoC) and a wireless communication module that transmits classification results to a smartphone app as a convenient user interface in real-time training. The customized SoC provides ultra-low-power and low-latency sensing and classification on physiological signals, e.g. EMG and ECG. A special collaborative neural network classifier was implemented to allow multiple chips to collaborate on classification. As a result, only low dimensional data is being transmitted over the network, significantly reducing data communication across multiple modules. A demonstration of EMG based gesture classification shows 1100X less power consumption from the developed SoC compared with conventional embedded solutions. The transmission of only low dimensional data from the collaborative neural network classifier leads to a 50X reduction of data communication and associated energy for multiple sensing cites.