Assessing quality of distorted/decompressed images without reference to the original image is a challenging task because extracted features are often inexact and no predefined relation exists between features and visual quality of images. The paper aims at assessing quality of distorted/decompressed images without any reference to the original image by developing a robust system using fuzzy relational classifier. First impreciseness in feature space of training data is handled using fuzzy clustering method. As a next step, logical relation between the structure of data and the quality of image are established. Quality of a new image is assessed in terms of degree of membership of the pattern in the given classes applying fuzzy relational operator. Finally, a crisp decision is obtained after defuzzification of the membership value.