Monitoring color in the production line requires to remotely observe moving and not-aligned objects with in general complex surface features: multicolored, textured, non-flat, showing highlights and shadows. We discuss the use of color cameras and associated color image processing technologies for what we call 'imaging colorimetry.' This is a 2-step procedure which first uses color for segmentation and for finding Regions-of- Interest on the moving objects and then uses cluster-based color image processing for computing color deviations relative to previously trained references. This colorimetry is much more a measurement of aesthetic consistency of the visual appearance of a product then the traditional measurement of a more physically defined mean color vector difference. We show how traditional non-imaging colorimetry looses most of this aesthetic information due to the computation of a mean color vector or mean color vector difference, by averaging over the sensor's field-of-view. A large number of industrial applications are presented where complex inspection tasks have been solved based on this approach. The expansion to a higher feature space dimensions based on the 'multisensorial camera' concept gives an outlook to future developments.