Coffee is a beverage obtained from cherry, the fruit of coffee plant. Grading serves as a process for controlling the quality of an agricultural commodity so that buyer and seller can do business without personally examining every lot sold. This study attempts to apply image processing techniques towards sample coffee raw quality value grading. A total of 145 image datasets and 10,000 coffee beans were used from different grade of coffee plant from ECX Jimma center. The experimental research design was employed. ImageJ tool and Matlab programming language were used. For image preprocessing Gaussian filter to remove noise, contrast enhancement method to enhance the quality of coffee bean image, normalization and binarization by thresholding 8-bit images algorithm to separate image into region in image segmentation process were used. Techniques and algorithms such as ANN, SVM and KNN were used in this study. For the purpose of computing the grading accuracy of datasets, 80% of total dataset were used for training the model and the remaining 20% of dataset were used for testing. The major challenges during conducting this study were keeping the best quality control environment when acquiring images, extracting best features of HSB color feature and the homogeneity of coffee plant bean color features. Hence, appropriate selection of image processing and classification modules paves the way for higher accuracy in the higher-level process for decision making.