To quantitatively predict coronary artery diseases, automated analysis may be preferred to current visual assessment of left ventricular (LV) wall motion. In this paper, a novel automated classification method is presented which uses shape models with localized variations. These sparse shape models were built from four-chamber and two-chamber echocardiographic sequences using principal component analysis and orthomax rotations. The resulting shape parameters were then used to classify local wall-motion abnormalities of LV segments. Various orthomax criteria were investigated. In all cases, higher classification correctness was achieved using significantly less shape parameters than before rotation. Since pathologies are typically spatially localized, many medical applications involving local classification should benefit from orthomax parameterizations.