Diabetic retinopathy is a disease that affects the eye of a diabetic patient. Diabetics beingrnthe abnormal level of glucose in the human blood, it is a cause for several types of organrndisorders in the body. One of them being the eye. Diabetic retinopathy is caused when the bloodrnvessels in the eye start liking blood in to the surface of the eye. Detection of diabetic retinopathyrnat different stages of the disease is an invaluable input for physicians in the medical field. rnIn this papera research has been made for detection of diabetic retinopathy exudates byrnusing an artificial neural network. The process of image detection starts from image preprocessing,rnrnnext is image segmentation then feature selection and finally classification. In thernimage pre-processing a median filtering and adaptive histogram equalization is used. The imagernblood vessels and Optic disk are then segmented out using morphological analysis. rnAccuracy of segmentation of the optic disk is 90%, 87% and 90 % for exudatesrncontaining images, normal images and all images that contain all features of diabetic retinopathyrnrespectively. rnThe gray level co-occurrencematrix of the image is then calculated to evaluate the textualrnfeatures. Fourteen features are then used to feed the artificial neural network. Two fundus imagerndata sets(STARE 402 images, and DIABETRETDB1 with 130 images) were obtained from twornmedical universities have been used for the detection process. Using the proposed methodology,rna detection performance of 84% sensitivity and 63% specificity is obtained.The implementationrnis done by using mathlab 2015.