This study examines the kinds of shape features that mediate basic- and subordinate-level object recognition. Observers were trained to categorize sets of novel objects at either a basic (between-families) or subordinate (within-family) level of classification. We analyzed the spatial distributions of fixations and compared them to model distributions of different curvature polarity (regions of convex or concave bounding contour), as well as internal part boundaries. The results showed a robust preference for fixation at part boundaries and for concave over convex regions of bounding contour, during both basic- and subordinate-level classification. In contrast, mean saccade amplitudes were shorter during basic- than subordinate-level classification. These findings challenge models of recognition that do not posit any special functional status to part boundaries or curvature polarity. We argue that both basic- and subordinate-level classification are mediated by object representations. These representations make explicit internal part boundaries, and distinguish concave and convex regions of bounding contour. The classification task constrains how shape information in these representations is used, consistent with the hypothesis that both parts-based, and image-based, operations support object recognition in human vision.