Shear stress is known to have a significant effect on the state of cellular differentiation. It also induces morphologic responses including changes to cytoskeletal organization subsequently leading to changes in cell shape. In fact, fluid shear stress caused by blood flow is a major determinant of vascular remodeling and can lead to development of atherosclerosis. The morphological changes are usually evaluated using boundary-based shape descriptors or binary geometrical moments on manually segmented cells. Although any one of the many automated segmentation methods could be employed, these techniques are known to be complex and time consuming, and often require user input to operate properly, which is especially problematic for HCS systems. Therefore, development of robust, quantitative morphological measurements that are not dependent on precision and reproducibility of segmentation is extremely important for a substantial improvement of shear-stress analysis. The goals of this study were to find simple morphological descriptors that could be applied to cells isolated by tessellation in order to enable a high-throughput screening of morphological shear-stress response, and to determine the amount of fluid shear stress to which endothelial cells were exposed on the basis of changes in their morphology. The proposed technique is based on the monitoring of changes in cytoskeleton organization using texture descriptors, rather than on quantifying cell-boundary modifications. We showed that objects identified by Voronoi tessellation carried enough information about cytoskeleton texture of individual cells to create a robust classifier. Our approach provided higher discriminant and predictive powers, and better classification capability, than traditional boundary-based methods. The robustness of classification in the presence of segmentation difficulties makes the proposed approach particularly suitable for automated HCS systems.