Tool Condition Monitoring (TCM) systems have become increasingly important in industrial automation due to the need to improve efficiency and reduce manufacturing costs. These systems use advanced sensors to capture signals during machining processes, allowing for the early detection of faults and prediction of tool life. This study explores the potential of using the cosine similarity (CS) method as a practical technique for analyzing acoustic emission (AE) signals and monitoring tool wear during milling operations. Acoustic signals were applied to the CS method under reference conditions and after potential damage. We used 9000 samples of the milling cutter passing over the workpiece, collected from experiments with milling machines using the AE sensor WD925 at a frequency of 100 kHz. The CS method tracked wear proportionally in each case. As the tool wore down, its similarity to the intact tool decreased, proving to be an effective indicator for condition monitoring. However, the change in CS calculation was not as pronounced as the tool wear observed, suggesting that having enough data is crucial for this methodology in condition monitoring. A longer sampling period is necessary to capture significant signal variations and effectively detect losses in similarity. This provides a significant amount of data and, as a result, leads to more conclusive findings for the process in question.