Technical Abstract: There are a number of x-ray imaging techniques that could be implemented for the detection of pits in cherries, including linescan and real-time imaging using an image intensifier and CCD camera. However, x-ray imaging equipment is both expensive and bulky, and implementation on the processing line would be cumbersome. Here, a simple one dimensional x-ray detector is tested for the ability to differentiate between pitted and unpitted fresh cherries. Acoustic impact analysis, a technique based on the difference in sounds generated as samples of different classes strike a solid surface, is also tested for the ability to detect pits in cherries. For the one dimensional x-ray detector, the results indicated almost perfect detection of pits with an average false positive rate of around 3%. For impact acoustics, a discriminant analysis routine was able to separate pitted from unpitted cherries in the validation set with 97% accuracy.