This paper proposes a method of detecting affected segments of glaucoma from optical coherence tomography (OCT) images. Thickness of nerve fiber layers and its asymmetry, difference, and variance are used as features. OCT images are segmented and the four features are obtained at each segment. Normal and glaucoma classes are constructed at each segment using training data. Detection of affected segments of glaucoma is carried out using four pattern classification methods : classification using Mahalanobis distance, maximum likelihood, nearest neighbor method, and support vector machine. The proposed method is evaluated by experiments to compare the detection results by the method and ones by a doctor.