Many weed species have reddish stems, but stems of wheat and soybean are green. These color features were used in this study to establish a simple weed-detection method using a color machine-vision system. This method is more practical than texture- or shape-based methods because of its low sensitivity to canopy overlap, leaf orientation, camera focusing, and wind effect. Four types of relative color indices formed by RGB gray levels were designed. The most effective combinations of these color indices were selected using a statistical method. These combinations were used as the input variables for a statistical classifier based on discriminant analysis (DA) and two artificial neural-network (NN) classifiers. These classifiers were trained and tested using three weed species (Johnsongrass, redroot pigweed, and yellow foxtail) with soybean and three weed species (wild buckwheat, cheat, and field bindweed) with wheat. Preprocessing and postprocessing algorithms were developed to shorten the processing time and to reduce noise. The results showed that the statistical DA classifier was more accurate than the NN classifiers in classification accuracy. The least-square means of the classification rates using the DA classifiers for soybean and wheat were 54.9% and 62.2%, respectively. The misclassification rates for most weed species were below 3%. Because the reddish colors on the stems of some weed species vary as the plants grow, an in-field calibration procedure will be needed to make the classifiers more adaptive to different circumstances.