Iris image analysis for clinical diagnosis is one of the most efficient non-invasive diagnosis methods for determining health status of organs. Correct and timely diagnosis is a critical, yet essential requirement of medical science. From the literature, it is found that modern technology also fails in lot of cases to diagnose disease correctly. The attempt is being made to explore the area of diagnosis from different perspectives. The approach used is a combination of ancestor’s technology Iridodiagnosis with modern technology. Iridodiagnosis is an alternative branch of medical science, which can be used for diagnostic purposes. To begin with a database is created of eye images with clinical history of subject’s emphasis on diabetic (type II) disease in pathological laboratory. The various algorithms are developed for image quality assessment, segmentation of iris, iris normalization and clinical feature classification for clinical diagnosis. The artificial neural network is used for training and classification purpose. The entire process shows classification accuracy of 90 ~ 92 percent between diabetic and non-diabetic subjects. A significant improvement is demonstrated in classification performance over the existing approaches. This approach will be useful in the diagnosis field which is faster, user friendly and less time consuming.