This thesis applies the process and knowledge of digital signal processing and image processing to diagnose diabetic retinopathy from images of retina. The Pre-Processing stage equalizes the uneven illumination associated with fundus images and also removes noise present in the image. Segmentation stage clusters the image into two distinct classes while the Disease Classifier stage was used to distinguish between candidate lesions and other information. Method of diagnosis of red spots, bleeding and detection of vein-artery crossover points were also developed in this work using the colour information, shape, size, object length to breadth ration as contained in the digital fundus image in the detection of this disease. In addition to diagnosis of Diabetic Retinopathy (DR), two graphical user interfaces (GUI’s) were also developed during this work, this first is for collection of lesion data information and was used by the ophthalmologist in marking images for database while the second GUI is for automatic diagnosing and displaying the diagnosis result in a more friendly user interface and is as shown in chapter three of this report. The algorithm was tested with a separate set of 25 fundus images. From this, the Receiver Operating Characteristics (ROC) was determined for red spot disease and bleeding, while cross over points were only detected leaving further classification as part of future work needed to complete this global project. Sensitivity (classify abnormal fundus images as abnormal) and specificity (classify normal fundus image as normal) was calculated for the algorithm is given as 98% and 61%.