Due to the limited information, weak anti-interference ability of a single sensor, and the long diagnosis time and low diagnosis rate of traditional network model，several sensors such as vibration and noise sensors were used to monitor the wear state of the milling cutter. A milling cutter wear condition signal monitoring method by combining deep learning and multi-sensor was proposed. The pattern recognition was carried out by inputting the screened and unscreened data into BP neural network, RBF neural network and deep convolutional neural network respectively, and the recognition results were compared and analyzed. The results show that the wear condition monitoring method combined with deep learning and multi-sensor has high diagnostic speed and accuracy when the characteristic quantity and the data quantity are large，and has obvious advantages in monitoring the wear condition of milling cutter.