Glaucoma is one of the leading cause of blindness but the detection at its earliest stage and subsequent treatment can aid patients to preserve blindness. The existing work has been focusing on global features such as texture, grayscale and wavelet energy of the Optic Nerve Head (ONH) and its surrounding to differentiate between normal and glaucoma images. In contrast to previous approaches which focus on global information only, this work proposes a new approach to automatically classify between the normal and glaucoma images based on Regional Wavelet Features of the ONH and different regions around it. These regions are usually used for diagnosis of glaucoma by clinicians through visual observation only. Our method automatically determines different clinically observed regions around the ONH and performs classification on the basis of wavelet energy at different frequency subbands. We have conducted experiments based on different global features and regional features respectively and applied it to RIMONE (An Open Retinal Image Database for Optic Nerve Evaluation) database with 158 images. The experimental evaluation demonstrated that the classification accuracy of normal and glaucoma images using Regional Wavelet Features of different regions with 93% outperforms all other feature sets.