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Background: Glaucoma is a common causes of blindness. The associated elevation in intra ocular pressure leads to progressive degeneration of the optic nerve and resultant structural changes with functional failure of the visual field. Since, glaucoma is asymptomatic in the early stages and the associated vision loss is irreparable, its early detection and timely medical treatment is essential to prevent further visual damage. Objective: This paper presents a novel method for glaucoma detection using digital fundus image and optical coherence tomography (OCT) image. Method: The first section focuses on the features such as cup to disc ratio (CDR) and the inferior superior nasal temporal (ISNT) ratio which were obtained from fundus images.The above features were used for classifying the normal and glaucoma condition using back propagation neural network (BPN) and Support Vector Machine (SVM) classifiers. In the second part of the article, features such as CDR and two novel features, cup depth and retinal thickness were obtained from the OCT image. These features were evaluated by the BPN and SVM classifier. Results and Conclusion: The combined features from fundus and OCT images were analyzed. The system proposed here is able to classify glaucoma automatically. The accuracy of BPN and SVM Classifiers was 90.76% and 96.92% respectively.