Tool wear condition monitoring (TWCM) has been a research hotspot for decades because of its significance for intelligent manufacturing. However, it has been little applicated in practice despites of its large number of publications. One of the crucial reasons is that the employed monitoring sensors, such as force, vibration, temperature and acoustic emission would disturb the nature processing conditions, which limited their applications in practice. The microphone sensor, which collect audible sound (AS) signal, is considered to be a prospective strategy for TWCM, because it has the advantages of closely related to tool wear, no need to attach sensors, convenient installation, flexible measurement, and no effect on the nature processing conditions. However, tool wear condition monitoring based on audible sound signal (TWCM-AS) is hindered by its insufficient accuracy. Therefore, this study serves as a resource for researchers and manufacturers by providing the recent trends in TWCM-AS. The generation mechanism of cutting AS through physical properties and practical experiments were concluded. Four key technologies, including acquisition, denoising, feature extraction and decision-making algorithm were discussed in detail. And future researches were point out, such as datasets, initiative denoising system, feature extraction of AS and interpretable decisionmaking algorithm.