Diabetic-retinopathy contributes to serious health problem in many parts of the world. With the motivation of the needs of the medical community system for early screening of diabetics and other diseases a computer aided diagnosis system is proposed. This work is aimed to develop an automated system to analyze the retinal images for important features of diabetic retinopathy using image processing techniques and an image classifier based on artificial neural network which classify the images according to the disease conditions. The consistent identifying and quantifying of changes in blood vessels and different findings such as exudates in the retina over time can be used for the early detection of diabetic retinopathy. The algorithm has been tested on an image data base and the results are presented. Vascular network, optic disc and lesions like exudates are identified. A neural network classifier is developed and a comparative study on the performance is also presented.