High machining accuracy is crucial in Computer Numerical Control (CNC) turning operations. Techniques such as thermal compensation, error prediction, and tool wear prediction rely heavily on high-quality machining signals to minimize errors effectively. However, identifying actual machining times from vibration signals is complicated by various factors, including different operations, tool types, manufacturing parameters, and external noise. This introduces significant challenges in subsequent analysis and process monitoring. This study presents an innovative framework for determining machining time intervals and durations using CNC programming language (G-code) and multiple sensor signals. By integrating G-code interpretation with synchronized data acquisition and signal segmentation, the approach enables automated identification of machining states, improving temporal alignment and feature extraction. The process begins by analyzing G-code to determine theoretical machining durations, followed by utilizing motor signals to detect the actual start and end points of machining operations. These identified time periods are then mapped to vibration signals, ensuring accurate segmentation of machining phases. Experimental validation demonstrates that the proposed framework enhances the precision and reliability of machining process monitoring while reducing the need for manual intervention.