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As a step to automate assembly of various industrial parts, this paper describes a versatile machine vision system that can recognize a variety of complex industrial parts and measure the necessary parameters for assembly, such as the locations of screw holes. Emphasis is given to a method for extracting useful features from the scene data for complex industrial parts so that accurate recognition of them is possible. The proposed method has the following features: 1) simple features are detected first in the scene and more complex features are examined later, using the locations of the previously found features; 2) the system is provided with a high-level supervisor that analyzes the current information obtained from the scene and structural models of various objects, and proposes the feartures to be examined next for recognizing the objects in the scene; 3) the supervisor has problem-solving capabilities to select the most promising feature among many others; 4) the structural models are used to suggest the locations of the features to be examined; and 5) several sophisticated feature extractors are used to detect the complex features. An effort is also made to make the system versatile so that it can be readily applied to a variety of different industrial parts. The proposed system has been tested on several sets of parts for small industrial gasoline engines and the results were satisfactory.