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A machine vision system was developed to inspect cut roses and sort into quality categories similar to those used by human inspectors. Image processing techniques were developed to find the base of the stem, the top of the bud, visible portions of the stem, and the projected area of the bud. Quantitative features were identified to analyze rose quality, including stem length, stem diameter, stem straightness, bud maturity, and bud color. Bayes decision theory was used to develop a classifier for straightness and maturity. Straightness was also classified by a neural network. Experimental tests were run on commercially produced yellow and white roses (Yellow Waves and White Mystery).