Recent advances in computer vision have allowed wide-ranging applications in every area of life. One such area of application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the extracted features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets is used in this work, where 70% of the dataset were used in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree classifier have been used to classifying fruits 360 images, results show that the the three feature extraction methods give a promising results , while the HoG method yielded a poerfull results in which the accuracy obtained is 96%.