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In machine work, the productivity, energy efficiency, and the quality of the work depend strongly on the skills of the human operator. This paper proposes a hierarchical method for skill evaluation of human operators in machine work during their normal work. The method refines skill metrics obtained from work cycle recognition-based evaluation system proposed earlier by the authors. The proposed skill components are: machine controlling skills, control parameter tuning skills, knowledge of the work technique and strategy, and planning and decision making skills. The skill components in each task are evaluated by a dedicated fuzzy inference system, whose rule base is generated automatically. The method is utilized to evaluate skills of nine operators of a cut-to-length forest harvester.