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CAD/CAM software products can help boost productivity for machining medical parts. However, the process of uating and re-calculating CAD model design is basically carried out manually. The demand for automated CAD process systems has been rising. Automated feature recognition (AFR) systems can improve system efficiency and effectiveness for processing CAD models in manufacturing sectors, particularly for designing medical machining parts. However, existing AFR methods are unable to fulfill industrial requirements for extracting and recognizing domain components CAD models efficiently. In this paper we suggest a knowledge-based AFR system that can efficiently identify domain components CAD models. The AFR knowledgebase incorporates rule-based methods for identifying core components CAD models. The process of defining the rules and fact base structure is one of the most critical issues in the AFR system design. There is no existing technology available for generating inference rules