Overall equipment effectiveness (OEE) is a key indicator to measure the operation status of machine tool, attracting much attention from manufacturing enterprises. However, the conventional approaches have the disadvantages of inconvenience, high cost and poor universality in calculating the availability, performance and quality rate indicators of OEE. Hence, an intelligent identification approach of OEE based on power information was proposed. Firstly, the time-frequency characteristics of machine tool power information were obtained, and the sampling period feature vector was established. Then, the principal component analysis was employed to construct the status matching library. Combined with the nearest neighbor algorithm, the running status was identified, and its duration was quantified to calculate the availability. In addition, the sliding window was applied to develop the processing period feature vector, and the distance matching was used to obtain the actual number of processing pieces. Combining the number of processing pieces with the data obtained by the MES system, the performance and quality rate were calculated. To verify the feasibility and practicability of the approach, the experimental study of the milling was performed, and the relative error between the theoretical value and the actual value is 4.99%.