Technical Abstract: The proportion of vitreous durum kernels in a sample is an important grading attribute in assessing the quality of durum wheat. The current standard method of determining wheat vitreousness is performed by visual inspection, which can be tedious and subjective. The objective of this study was to evaluate an automated machine vision inspection system to detect wheat vitreousness using reflectance and transmittance images. Two subclasses of durum wheat were investigated in this study, hard and vitreous of amber color (HVAC) durum wheat and not hard and vitreous of amber color (NHVAC). A total of 4,907 kernels in the calibration set and 4,407 kernels in the validation set were imaged using a Foss Cervitec 1625 Grain Inspector. Classification models were developed with stepwise discriminant analysis and a neural network (ANN). A discriminant model correctly classified 94.9% of HVAC and 91.0% of NHVAC in the calibration set, and 92.4% of HVAC and 92.7% of NHVAC in the validation set. The classification results using the ANN were not as good as with the discriminant methods, but the ANN only used features from reflectance images. Among all the kernels, mottled kernels were the most difficult to classify. Both reflectance and transmittance images were helpful in classification. In conclusion, the automated vision-based wheat quality inspection system may provide the grain industry with a rapid, objective, and accurate method to determine the vitreousness of durum wheat.