To solve the problem that the existing tool wear monitoring methods based on deep learning require too many samples and have low identification accuracy,a tool wear monitoring model based on transfer learning (TL) and deep residual network (ResNet) was established.The vibration monitoring signals during tool machining were converted into energy time-frequency diagram by continuous wavelet transform,which were used as the input of network model.The ResNet50 model trained on ImageNet dataset was used as the pre-training model,and was applied to the field of tool wear state monitoring by transfer learning method.The example verification shows that the recognition accuracy of TL-ResNet reaches 98.52%,realizing intelligent recognition under different tool wear states.So the accuracy and stability of tool wear state monitoring are improved effectively.