Consistent production of components meeting high-quality standards requires high production process reliability. In the case of mechanical material removal, high reliability requires monitoring the state of wear of the cutting tool, as it affects the surface quality of the workpiece. A predictive maintenance process can be implemented by monitoring tool wear in order to reduce the number of low-quality pieces. The use of indirect methods carries significant potential in this field, as it enables detecting the cutting tool's condition in real-time. The main challenge in developing indirect methods for monitoring tool wear is characterizing the relationship between wear and the measured quantity. In this work, we developed a method of indirect wear measurement by monitoring the tool's vibration response. The measured signals were split into individual components by the MSSA (Multi-channel Singular Spectrum Analysis) method for time series decomposition. Based on selected estimators of the distribution of MSSA components of the vibration response, the degree of wear of the cutting tool was identified using neural networks through a regression approach and classification. It was demonstrated that the distribution parameter estimators of MSSA components enable more accurate identification of wear than estimators of the measured signals.