Tool Condition Monitoring (TCM) is vital for maintaining productivity and product quality in machining. This study leverages machine learning to analyze real-time force signals collected from experiments under various tool wear conditions. Statistical analysis and feature selection using decision trees were followed by classification using a K-Nearest Neighbors (KNN) algorithm, with hyperparameter tuning to enhance performance. While machine learning has been widely applied in TCM, interpretability remains limited. This work introduces a KNN-based white-box model that enhances transparency in decision-making by revealing how features influence classification. The model not only detects tool wear but also provides insights into the reasoning behind each decision, enabling manufacturers to make informed maintenance choices.