In milling machining engineering, chattering is generated, which seriously affects the machining accuracy and surface quality of products. In order to effectively avoid chattering during milling, a milling chattering monitoring and identification method based on Adaptive chirp mode pursuit (ACMP) is proposed. The method integrates the bandwidth and faint characteristics of vibration signals, and ACMP captures the signal modes one by one in a recursive framework. In this algorithm, we do not need to input the number of signal modes, but can learn them by evaluating the energy of the residual signal, so that we can avoid the problems of modal mixing or over-decomposition due to the uncertainty of the number of decomposition layers. Firstly, the algorithm is verified to have high recognition accuracy for chattering signals using simulated signals; then the method is demonstrated to be effective in recognizing chattering in time based on field milling experimental data; finally, the power spectrum entropy value is extracted from the ACMP processed signals as chattering recognition features. This method solves the modal mixing and pseudo-component problems of empirical mode decomposition (EMD) algorithm, and reduces the influence of unstable accuracy of variational mode decomposition (VMD), which can accurately and quickly identify the chattering and is of great significance to improve the machining quality.