Estimation of maturity stage of mangosteen during harvest is critical because it affects the quality of mangosteen. Currently, the estimation is manually performed by labor using visual method. Since the visual method is not precise and consistent, the use of image processing technology promises a better result. The objective of this research is to develop an image processing method to optimize estimation of mangosteen maturity stage by combining Red-Green-Blue (RGB) color features and implementing Support Vector Machine. The methodology involves fruit collection, image acquisition and image processing step. In the image processing step, color features i.e. sum, mean and standard deviation of R, G and B component were extracted from images. These features were then combined and used as parameter input for SVM training-testing. The proposed method yielded a significant improvement on maturity stage estimation and was able to increase its accuracy up to 91% using combination of nine features.