In this paper, we propose a new method for automated detection and segmentation of different tissue types in digitized uterine cervix images using mean-shift clustering and support vector machines (SVM) classification on cluster features. We specifically target the segmentation of precancerous lesions in a NCI/NLM archive of 60,000 cervigrams. Due to large variations in image appearance in the archive, color and texture features of a tissue type in one image often overlap with that of a different tissue type in another image. This makes reliable tissue segmentation in a large number of images a very challenging problem. In this paper, we propose the use of powerful machine learning techniques such as Support Vector Machines (SVM) to learn, from a database with ground truth annotations, critical visual signs that correlate with important tissue types and to use the learned classifier for tissue segmentation in unseen images. In our experiments, SVM performs better than un-supervised methods such as Gaussian Mixture clustering, but it does not scale very well to large training sets and does not always guarantee improved performance given more training data. To address this problem, we combine SVM and clustering so that the features we extracted for classification are features of clusters returned by the mean-shift clustering algorithm. Compared to classification using individual pixel features, classification by cluster features greatly reduces the dimensionality of the problem, thus it is more efficient while producing results with comparable accuracy.