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• A new feature extraction method - multiscale standard deviation diversity entropy (MSDDE) is proposed. MSDDE has better stability at high scales. • The method meets the requirements of real-time tool breakage detection in terms of signal sampling length and computational efficiency. The sampling length only needs to contain two spindle rotation periods, and the computational efficiency of the method reaches the millisecond level. • The method is insensitive to the changes of machining parameters including feed rate and cutting width, and the classification accuracy of the corresponding support vector machine (SVM) models can reach 99%. Tool breakage monitoring (TBM) during milling is vital for ensuring product quality, equipment and operator safety. This paper proposes a novel real-time TBM method based on multiscale standard deviation diversity entropy (MSDDE). Unlike existing similar works that only validate the effectiveness of the extracted features while ignoring the requirements of real-time detection, the proposed method can extract effective features from short data samples containing only two spindle rotation periods and achieve computational efficiency in milliseconds. Consequently, it is more suitable for application in the real-time detection of milling tool breakage. MSDDE quantifies the dynamic complexity of vibration signals at different time scales, which is capable of reducing the requirement for the number of spindle periods in the sliding time window while comprehensively describing the tool breakage characteristics. The proposed algorithm exhibits a time complexity of O(MN) where M depends on the scale parameter and embedding dimension. Experimental investigations show that the proposed method is able to complete the calculation of feature values within two milliseconds and realize real-time detection of milling tool breakage. [Display omitted] [ABSTRACT FROM AUTHOR]