Condition monitoring provides insights into the type of damage occurring in the cutting tool during machining to facilitate its timely maintenance or replacement. By detecting and analyzing machining consequences (vibrations, chatter, noise, power consumption, spindle load, etc.), correlating them with different tool conditions enables real-time monitoring and the automated detection of tool failures. Machine learning (ML) plays a vital role in making tool condition monitoring (TCM) frameworks intelligent, and most research is geared toward classifying various types of tool wear. However, monitoring built-up edges, chipping, thermal cracking, and plastic deformation of milling cutter inserts are challenging and need careful consideration. To effectively monitor these phenomena, spindle vibrations can narrate the corresponding dynamic behavior of tool conditions and therefore have been investigated in this research. The acquired vibration data are then analyzed using histogram features and trained through the Partial C4.5 (PART) classifier to extract meaningful recommendations related to the milling cutter inserts condition.