Due to the high efficiency and consistent product quality, CNC machining has gained a dominant role in the modern machining industry. However, conventional machining still has its significance for certain production settings, e.g. prototyping, and machining workforce preparation. CNC and conventional machining require particular skills and knowledge, which can be unique to specific types of machine tools or overlap to a certain extent. With increasing production volume demands and an aging workforce, a need for efficient quantification, preservation and transfer of said skillsets arises to ensure effective preparation of future professionals and the undisrupted operation of the manufacturing industry. Moreover, the observed shift towards human-centered manufacturing systems in the Industry 4.0 necessitates obtaining an in-depth understanding of human roles in machining. The following paper proposes a novel research approach based on collection and analysis of eye-tracking and video data supplemented by verbal interviews, surveys and self-assessment. A conducted case study spans the entirety of the machining process, from part evaluation, cutting strategy determination, machining operations, to process re-evaluation and optimization. The results show that far greater variability in cutting strategy in terms of operation order, number of operations and used production tooling between consecutive production runs can be observed for conventional machining, with little variation in those terms noted for CNC-based production. Overall, the collected data has allowed to gain an insight into the machinist's decision-making processes and the rationale for observed cutting strategy changes, allowing for potential future application of the proposed research method in improving the machining training and potentially aiding process design by applying the outcomes of studies performed with the use of presented research method to expert systems and future CAM/CAE software solutions.