Abstract: Automatic modulation recognition (AMR) has been one of the key methods to ensure licit communications, and plays a key role in various civilian and military applications. In this paper, a new distributed cooperative recognition method is proposed to recognize different digital modulation types with multiple sensors in wireless sensor networks(WSNs). In order to enhance the successful recognition rate in fading channel when SNR is low and realize correct recognition of several classic modulation types such as MASK, MFSK, BPSK, QPSK and OFDM, effective cooperative methods are designed according to SNR of received signal and based on the principle of lowest sensor overhead. A new combination of features is extracted accordingly by several collaborated sensors to improve the performance of the modulation recognition system. Then the features are sent to the Radial Basis Function(RBF) neural network so that modulation types can be recognized. Further more, different cooperative methods introduced in this paper are adaptive to the condition of the sensor networks. To measure the performance of the proposed methods, simulations are carried out to classify different types of modulated signals in fading channels. The simulation results show that the proposed distributed cooperative algorithm has higher recognition rates with better system reliability compared with that without cooperation.