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For pearls and other smooth alike lustrous jewels, the apparent shininess is one of the most important factors of beauty. This paper proposes an approach to automatic assessment of spherical surface quality in measure of shininess and smoothness using artificial vision. It traces a light ray emitted by a point source and images the resulting highlight patterns reflected from the surface. Once the reflected ray is observed as a white-clipping level in the camera image, the direction of the incident ray is determined and the specularity is estimated. As the specular exponent is the most important reason of surface shininess, the method proposed can efficiently determine the equivalent index of appearance for quality assessment. The observed highlight spot and specular exponent measurement described in this paper provide a way to measure the shininess and to relate the surface appearance with white-clipped image highlights. This is very useful to industrial applications for automatic classification of spherical objects. Both numerical simulations and practical experiments are carried out. Results of objective and subjective comparison show its satisfactory consistency with expert visual inspection. It also demonstrates the feasibility in practical industrial systems.