Therefore, the time-frequency domain analysis represented by wavelet transform becomes a powerful tool. [PERSON] used the wavelet packet transform to extract the feature vector from the tool-holder vibration signal to identify the machinability of the nickel-titanium alloy turning process, and the recognition accuracy is not less than 80% [23]. [PERSON] employed dual-tree complex wavelets to extract features from sound pressure signals to monitor the health of indexable inserts [15]. [PERSON] proposed a sparsity-based feature extraction method using the tunable Q-factor wavelet transform with dual Q-factors [24]. [PERSON] adopted wavelet transform to decompose the original monitoring signals, and extract features from each sub-signal to form a mixed feature vector. The trained RF model identifies the wear state of the drill with an accuracy of no less than 96% [25]. [PERSON] applied the wavelet packet decomposition to the low SNR cutting force signal in the micro end milling process, which effectively improved the feature extraction efficiency [26]. The decision subsystem is the most important part of achieving tool condition monitoring. It is a complex nonlinear model that identifies the health condition of the tool based on the feature vector. Various machine learning models have succeeded in the field, such as artificial neural network (ANN) [27], fuzzy inference systems (FIS) [28], hidden Markov model (HMM) [29] and support vector machine (SVM) [30] and others [31; 32; 33]. The special network structure makes the machine learning model also able to achieve ideal classification accuracy when the data set is small, but they are not suitable for data samples with big size. This imposes stringent requirements on feature engineering. In addition to the evolutionary factors we expect to monitor, other operational parameters of the monitored object will also affect the features extracted by the feature engineering. [8]. For the milling process, not only tool wear, but also cutting condition adjustment and changes in work piece material properties have significant effects on statistical features such as RMS, kurtosis and entropy. The portability of the system is thus reduced and is not suitable for automated production sites where cutting conditions are variable. The new learning algorithms empower us to build neural networks with more layers, as well as train them with big samples. Coupled with the extremely increasing computing power, especially parallel computing, the powerful feature extraction capabilities of convolutional neural network (CNN) have been validated in many pattern recognition [34; 35] and phonetic recognition projects [36; 37]. The monitoring signal such as vibration acceleration is similar to the speech signal, and the line graph or time spectrum of the signal is similar to the visual image. Therefore, many scholars adopt the CNN to equipment fault diagnosis and health monitoring. [PERSON] adopts CNN to the identification of gear faults, and the recognition rate reached 99.97% under the enhancement of double-tree complex wavelet [38]. [PERSON] proposed an adaptive convolutional neural network for fault diagnosis of rolling element bearings, which proved to be superior to ANN and SVM [39]. [PERSON] mapped the original monitoring signals into feature maps to fit the 2-D convolutional neural network, and the recognition accuracy of bearing faults was not less than 90% [40]. [PERSON] mapped the raw 1-D signals into 2-D images to identify the vibration state of the machine tool. The recognition accuracy of the 2-D CNN is not less than 90%, which is obviously superior to the traditional signal-feature-model [41]. This paper proposes a tool condition monitoring system based on 2-D convolutional neural network and assisted by complex wavelet. The reminder of this paper is organized as follows. In Section 2, some background and preliminaries are reviewed. In Section 3, we detailed the proposed monitoring system, including data acquisition, data preprocessing and methods for constructing 2-D maps.