The study of the superficial white matter and its description is essential for the understanding of human brain function and the study of pathogenesis. However, the study of these fibers is still an incomplete task due to the high inter-subject variability and the size of this kind of fibers. In this work, a superficial white matter bundle identification based on fiber clustering was performed using probabilistic tractography on 100 subjects from the The Human Connectome Project (HCP) data, aligned with a non-linear registration. The method starts with an intra-subject clustering, followed by a segmentation of fibers connecting the precentral (PrC) and postcentral (PoC) regions, based on a ROI atlas. Due to the high amount of fibers, they were randomly separated into groups. An inter-subject clustering was applied on the fibers of each group, and then two clustering levels were applied to select the most reproducible bundles. Seven bundles per hemisphere were obtained, connecting the PrC and PoC regions. These were compared with bundles from previous atlases, showing in general more coverage and some bundles not found in previous atlases.