Quality control is a key requirement of the Fourth Industrial Revolution, especially in manufacturing. For high-value technical parts, the geometric and appearance quality need to be guaranteed. The control of the part, directly on the manufacturing line, can be achieved with noncontact sensors. Visible spectrum imaging is commonly used. However, more information could be obtained from broader spectrum imaging and from the degree of linear polarization of the light from the part. We investigate polarimetry. We have developed a simple polarimetric imaging system to verify whether linear polarization is capable of amplifying geometric and appearance defects. We evaluate the use of this polarimetric imager given the industrial constraints of injection molding. We compare the performances of different machine learning pipelines with hand-crafted feature extraction and deep transfer learning. With its industrial robustness, polarimetry could be a valuable addition to noncontact imagers for geometric and appearance quality control of plastic molded parts.