The presence of pits in processed cherries is a concern for both processors and consumers, in many cases causing injury and potential lawsuits. While machines used for pitting cherries are extremely efficient, if one or more plungers in a pitting head become misaligned, a large number of pits may pass before corrective action is taken. While x-ray imaging has the potential to detect pits, traditional commercially available equipment is expensive and bulky, and implementation on the processing line is cumbersome. An x-ray inspection system using an array of photodiode based x-ray detectors in a linescan configuration whose outputs are combined to produce a one dimensional signal would be simpler, faster, and more economical. The data collection process is then reduced from a two dimensional image to a much simpler one dimensional signal, resulting in faster and simpler processing and classification. An algorithm designed to differentiate unpitted from pitted cherries for such a system yielded recognitiothe unpitted cherries, with a total error rate of 3.5%. When the a of pitted fruit, 100% of pitted cherries were detected with a orientation is controlled after pitting, total error is reduced to 1%.