Mosaic vasculature is one crucial visual sign often indicating the existence of abnormality in the underlying cervix tissues. Automatic detection of this vascular pattern in uterine cervix images is a challenging task, especially in a large dataset, due to the factors such as fuzzy boundary, small vessel caliber, and appearance variation. In this paper, we present a supervised-learning based approach to segment the regions encompassing mosaic vasculatures, hoping to overcome these challenges. It is part of an automatic segmentation scheme that is aimed at assisting gynecologists in the study of cervical cancer. The affectivity of the method was tested and evaluated on a set of clinical uterine cervix images that were manually marked and categorized by medical experts.