Glaucoma is one of the eye diseases that can lead to the blindness if not detected and treated at proper time. This paper presents a novel technique to diagnose glaucoma using digital fundus images. In this proposed method, the objective is to apply image processing and machine-learning techniques on the digital fundus images of the eye for separating glaucomatous eye from normal eye. Image preprocessing, techniques such as noise removal and contrast enhancement are used for improving the quality of image thus making it suitable for further processing. Statistical feature extraction methods such as Gray-Level Run Length Matrix (GLRLM) and Gray-Level Co-occurrence Matrix (GLCM) are used for extracting texture features from preprocessed fundus images. Support Vector Machine (SVM) classification method is used for distinguishing glaucomatous eye fundus images from normal, unaffected eye fundus images. The performance of the trained SVM classifier is also tested on a test set of eye fundus images and comparison is done with other existing recent methods of Glaucoma detection. © Springer Nature Singapore Pte Ltd 2018.