We have developed a computational method based on a family of geometric measures for the purpose of classification and identification of families of sulcal curves from human brain surfaces. Geometric measures involving combinations of writhe, average crossing number, ropelength and thickness of sulcal curves are computed to obtain a set of feature vectors in a high dimensional vector space. We then reduce the dimensionality of these vectors to find an optimal planar projection in order to identify significant clusters. Human brain surfaces were extracted from MRI scans of human brains. In our preliminary results, an automatic differentiation between sulcal paths from the left or right hemispheres, an age differentiation and a male-female classification were achieved.