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Shape models (SMs), capturing the common features of a set of training shapes, represent a new incoming object based on its projection onto the corresponding model. Given a set of learned SMs representing different objects classes, and an image with a new shape, this work introduces a joint classification-segmentation framework with a twofold goal. First, to automatically select the SM that best represents the object, and second, to accurately segment the image taking into account both the image information and the features and variations learned from the online selected model. A new energy functional is introduced that simultaneously accomplishes both goals. Model selection is performed based on a shape similarity measure, online determining which model to use at each iteration of the steepest descent minimization, allowing for model switching and adaptation to the data. High-order SMs are used in order to deal with very similar object classes and natural variability within them. Position and transformation invariance is included as part of the modeling as well. The presentation of the framework is complemented with examples for the difficult task of simultaneously classifying and segmenting closely related shapes, such as stages of human activities, in images with severe occlusions.