In this paper, the cutting force and vibration signals in different axial directions and the RMS of the acoustic emission signal in the milling of the high speed CNC cutters are fully utilized to evaluate the tool wear in the data-driven method. In this study, the sensitive features related to tool wear are explored from three aspects: time-domain, frequency-domain and joint time-frequency domain, and the feature extraction methods include time-domain statistical analysis, fast Fourier transform (FFT) between time-domain and frequency-domain, and wavelet transform (WT) in time-frequency domain. In this paper, the decision tree will be used for regression problems, rather than classification issues, to assess the tool wear value. And then, the AdaBoost algorithm is introduced to improve the performance of the decision tree regression (DTR), and the performance of the adaptive boosted decision tree regression (DTR-Ada) model and the original model are compared at the aspects of the accuracy, steadiness and applicability. The result shows the DTR-Ada model can improve the accuracy and stability of the fitting and prediction, and it also achieves a good effect on the applicability of the new tool wears prediction.