To study how to accurately recognize clear images and blurred images at different degrees, three characteristics of images were firstly extracted from the following three aspects: statistical characteristic of frequency coefficient, and kurtosis coefficient and saturation in Discrete Cosine Transform. Then a blurred image could be recognized by support vector machine (SVM) from the library of blurred images at different degrees. The combination of the above three image characteristics were very suitable for describing images blur. And the Support Vector Machine could distinguish Gaussian Blur images and clear images very accurately and rapidly. Extraction of the typical characteristics of the blurred images can be applied to the recognition of images with different degrees of blur. And the accuracy of support vector machine classification is relatively high. Therefore, this method can be applied to the early stage of the images processing, to eliminate some blurred images that hinder information expression.