Abstract: In order to assess the quality of seed cottons objectively, sorting classifiers were designed based on machine vision technologies to grade 305 seed cottons with 7 grades based on their size and adjusted colors according to Chinese government grading standards. Fisher-criterion based canonical discriminants show that size and impurity contributed much more for cotton grades, and the distances among high-grades centroids were long while the ones among low-grades centroids were short. Total samples were divided into the train set and the test set. Cross-validation and Bayes-criterion based classifiers selections on the train set show that various classifiers were selected on 10-fold validation set with accuracies from 75% to 93%, and the approximate optimized classifiers were selected according to their average accuracy of 83%. Classifier performances evaluations on the test set show that the optimized classifiers can classify cottons into 7 grade categories with an accuracy of nearly 88%, and the high-grades cottons from 1 to 3 can be discriminated with an accurary of 100%. It is feasible to classify cotton grades using machine vision technologies and it helps to improve the yield of high-quality cottons.