In addition, with the development of the sensor and the modern signal processing technology, chatter identification technology has been greatly improved. Various signals have been applied in chatter identification, such as cutting force [14; 15; 16], sound [17] and the acceleration [18; 19]. [PERSON] et al. presented an effective chatter identification method based on the two advanced signals processing techniques, wavelet package transform (WT) and Hilbert-Huang transform (HHT), and the experimental results proved that the method can identify the chatter effectively [20]. In 2015, [PERSON] et al. adopted a self-adaptive analysis method named ensemble empirical mode decomposition (EEMD), extracted two nonlinear indices as the indices of chatter symptoms, and presented a method for identifying the chattering phenomenon based on the two points in the end milling process. The results have shown that the mean value and standard deviation increase with the cutting state's instability [21; 22]. The vibration signals belong to unsteady and nonlinear signals in the 3D-EVC process [23], traditional time-frequency analysis methods can analyze the global mean of the signal in the time-frequency domain, but it cannot analyze the feature information about the local area. In order to solve the lack of self-adaptability or poor adaptability in traditional time-frequency analysis methods, [PERSON] et al. proposed a novel and adaptive decomposition method, which was named empirical mode decomposition (EMD), in 1998 [24]. [PERSON] et al. decomposed the motor current signal into intrinsic mode functions (IMFs) and extracted the energy index and kurtosis index based on those IMFs for chatter detection [21]. Compared with the traditional time-frequency analysis, EMD is more appropriate for analyzing the non-stationary and nonlinear signals, as the EMD method can not only yield clear distribution for instantaneous frequency and amplitude, but also reflect the time-variation of the total energies, which is significant for the non-stationary and nonlinear signals. In recent years, the EMD method has been applied to signal processing, fault diagnosis [25] and pattern recognition [25; 26]. Based on the above research, this paper presents chatter identification based on the empirical mode decomposition (EMD) technique and feature extraction. The method of chatter identification developed is divided into two parts: initial identification and feature extraction identification. In 3D-EVC operations, the vibration signals are collected by displacement sensors and converted to frequency domain signals by fast Fourier transform (FFT), and the cutting state of the whole machining system is determined according to the change of frequency. The vibration signals are decomposed by EMD, a series of intrinsic mode functions (IMFs), so the instantaneous frequency can be reflected by the vibration signals at any point. Selecting the primary IMFs which contain rich chatter information as the object in feature extraction identification, two identification indexes, that is, the mean square frequency (MSF) and self-correlation coefficient (\(\rho_{1}\)), were calculated from the primary IMFs using MATLAB software to judge the chatter phenomenon. In addition, chatter identification is organized as follows. First step is the fast Fourier transform (FFT) analysis of the time domain signal to observe whether the frequency changes during the cutting process. If there is a frequency domain mutation, it indicated that chatter occurs during the cutting process. The second step is eigenvalue identification, which serves two purposes. One needs to test the chatter identification result of the initial step by using two distinct eigenvalue changes during the cutting process. More importantly, the eigenvalue can reflect the state change of the cutting process to a certain extent, which is used as a theoretical basis for the online identification of chatter. This paper is structured as follows.