A computer vision system was developed for evaluation of the total damage factor used in corn grading. Major categories of corn damage in the Midwestern U.S. grain market were blue–eye mold damage and germ damage. Seven hundred twenty kernels were obtained from officially sampled Federal Grain Inspection Service (FGIS) corn samples and classified by inspectors on the Board of Appeals and Review. Inspectors classified these kernels into blue–eye mold, germ–damaged, and sound kernels at an 88% agreement rate. A color vision system and lighting chamber were developed to capture replicate images from each sample kernel. Images were segmented via input of red, green, and blue (RGB) values into a neural network trained to recognize color patterns of blue–eye mold, germ damage, sound germ, shadow in sound germ, hard starch, and soft starch. Morphological features (area and number of occurrences) from each of these color group areas were input to a genetic–based probabilistic neural network for computer vision image classification of kernels into blue–eye mold, germ damage, and sound categories. Correct classification by the network on unseen images was 78, 94, and 93%, respectively. Correct classification for sound and damaged categories on unseen images was 92 and 93%, respectively.