Glasses detection is one of attractive tasks in image processing since it increases the performance of face recognition systems. In this study, we aimed to detect the glasses on face images automatically. In order to do this, we trained a classifier with Labelled Faces in the Wild Home(LFW) dataset to decide whether a person wear glasses or not on face images. Before classification process, image patches are extracted from aligned face images and a preprocessing was performed on them. After preprocessing step, feature vectors are formed with Histogram of Oriented Gradients (HOG) method from image patches. Due to high dimensionality of the feature vectors, dimensionality reduction was done using Principal Component Analysis(PCA). The dimension-reduced feature vectors were splitted into training set and test set. With training set images, Support Vector Machines(SVM) classifier was trained and the model parameters were defined. The classifier performance was evaluated with test set images and nearly 93% accuracy rate was achieved.