A prototype cut-flower sorter was developed and tested for its performance with five varieties of roses. Support plates driven by a chain mechanism transported the roses into an image inspection chamber. Color image processing algorithms were developed to evaluate the length, thickness, and straightness of stem and color, height, and maturity of bud. The average absolute errors of the system for the measurements of stem length, stem thickness, and height of bud were 19.7 mm, 0.5 mm, and 3.8 mm, respectively. The results of classification by the sorter were compared with those of a human inspector for straightness of stem and maturity of bud. The classification error for the straightness of stem was 8.6%, when both direct image and reflected image by a mirror were analyzed. The accuracy in classifying the maturity of bud varied among the varieties, the smallest for‘Nobless’(1.5%) and the largest for‘Rote Rose’(13.5%). The time required to process a rose averaged 2.06 seconds, equivalent to the capacity of 1,600 roses per hour.