An investigation was conducted to determine whether image processing and machine vision technology could be used for identification of the damage factor in corn kernels. Prominent types of corn kernel damage were found to be germ damage and blue-eye mold damage. A sample set containing 720 kernels with approximately equal numbers of blue-eye mold-damaged, germ-damaged, and sound kernels was obtained and evaluated by human inspectors and the computer vision system. While the computer vision system developed was slightly less consistent in classification than trained human inspectors, it did prove to be a promising step toward inspection automation;Two probabilistic neural network architectures were implemented. The first network, based on a universal smoothing factor algorithm, was used to segment the collected images into blue-eye mold-damaged, germ-damaged, sound germ, shadow in sound germ, hard starch, and soft starch areas. Morphological features from each of the segmented areas were then input to a second probabilistic neural network which used genetic algorithms to optimize a unique smoothing factor for each network input. Output of the second layer network was overall kernel classification of blue-eye mold-damaged, germ-damaged, and sound. Overall accuracy of classification on unseen images was 78%, 94%, and 93% for blue-eye mold-damaged, germ-damaged, and sound categories, respectively. Correct classification for sound and damaged categories on unseen images was 92% and 93%, respectively.