Computed tomography, a modern imaging technique commonly used for diagnosing human health ailments, utilizes multiple x-rays and sophisticated software to create a cross sectional radiograph of a subject. We investigated using this technique to image Hard Red Winter wheat samples infested with pupae of the rice weevil, and then developed a custom software program to rapidly recognize and quantify infested kernels in a given sample. Samples were imaged in 7.6 cm (dia) plastic tubes filled with one kg of wheat and densities of 0, 50, or 100 infested kernels. Spaces among wheat kernels were filled with corn oil to increase the contrast with kernels containing insects. Image analyses were conducted separately on each 100 g portion of the prepared samples. The average detection rate in the 5 infested kernels/100 g samples was 94.4  7.3% (mean SD) while the average detection accuracy in the 10 infested kernels/100 g sample was 87.3  7.9%. Detection accuracy in the 10 infested kernels/100g samples was slightly lower than the 5 infested kernels/100g samples due to some infested kernels overlapping with each other or air bubbles in the oil. In the control replicates, an average of 1.2  0.9 (n=10) bubbles was incorrectly classed as infested kernels. In light of these positive results, future studies will be conducted utilizing additional grains, insect species, and life stages.