Modern production facilities are large and highly complex, and they operate with numerous variables under closed loop control. In the production of green uranium pellets, pellet quality control involves many variables. Therefore, the classification of the quality of pellets is important for improving the efficiency of the production process. Classification of pellet quality using the conventional graphical method has some drawbacks; for example, the scale of the graph affects the accuracy and ease of use. In this paper, intelligent techniques are used to classify the quality of the pressurized water reactors(PWRs) green pellets into three categories according to the guidelines in the quality control manual of the experimental fuel elements laboratory of BATAN. Four features are used for classification, namely, height, volume, weight, density and theoretical density of the pellets. A dataset (150 observations) was collected from one lot of compacted UO 2 pellets and was used for training and testing of an ANFIS model. Up to 86.27% of the data can be classified correctly using the ANFIS model. Such performance is comparable to that of artificial neural networks. Thus, this model can be applied effectively for the evaluation and classification of pellet quality.