Objective To detect hard exudates automatically in fundus images，an detecting method based on improved fuzzy C-means (IFCM) and support vector machine（SVM） is proposed. Methods Firstly, 120 color fundus images gotten from Department of Ophthalmology, Jiangsu Province Hospital of  TCM were segmented by IFCM, and candidate regions of hard exudates were obtained. Then, the SVM classifer was established with the optimal subset of features which were selected by logistic regression and judgments of these candidate regions.Finally，hard exudates were automatically detected in 65 fundus images. Results Average sensitivity of 96.47% and average positive predict value of 90.13% were achieved with a lesion-based criterion. The sensitivity，specificity and accuracy were 100%,95% and 98.46%, respectively, with an image-based criterion. Average time in processing an image was 4.56 s. Conclusions The method based on IFCM and SVM with higher recognition rate can efficiently detect hard exudates in fundus images.