Plum is one of the nutritious and popular fruits in Iran. Depending on the quality of the fresh fruit before harvesting and the drying process, different quality grades of this fruit are produced as dried plums. In this research, a computer vision system and machine learning algorithms were used to classify dried plums into three different quality grades. Different color, shape, and texture features were extracted from the images of dried plum samples, and were used separately and in combination with each other for developing classification algorithms of Multilayer Perceptron (MLP) neural networks, Support Vector Machine (SVM), Linear Discrimination Analysis (LDA) and Decision Tree (DT). In order to reduce the number of features and to extract more important features, the Correlation-based Feature Selection (CFS) method was used. Results showed that the combination of different image-extracted features increases the classification accuracy, compared to individual color, shape, or texture features. In this regard, the Random Forest (RF) DT model using the combination of image features and CFS feature selection algorithm had the highest classification accuracy in the training and evaluation stages. The values of Root Mean Squared Error (RMSE) and accuracy of this model were obtained equal to 0.1958 and 93.75% in the training stage, and equal to 0.2110 and 91.67% in the evaluation phase, respectively. Considering these performance parameters and the nature of machine vision systems, the results of this research can be used to develop an accurate, fast, and inexpensive system for the quality grading of dried plums.