In high speed milling aeronautical part, tool condition monitoring (TCM) is very important, because it is prone to get a chatter owing to the low stiffness of thin-walled structures. And the TCM is key technology for automated machining. In this paper, aiming to chatter monitoring in thin-walled structure milling, a variational mode decomposition – energy distribution (VMD-ED) method is proposed to improve the identification accuracy. And a moving average root mean square – mean value (MARMS-MV) identification method and a variational mode decomposition – energy entropy (VMD-EE) identification method are also tested. Identification accuracy and computing time of the three methods are compared. The vibration signals collected from the spindle and worktable are also contrasted. The conducted experimental study shows that, the proposed VMD-ED method offers an identification method for chatter monitoring with greater sensitivity, better stability and less computing time, and mounting the vibration sensor on worktable is better than spindle for a chatter monitoring system.