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A common challenge in remote sensing is the classification of objects that are spectrally similar but represent physically different types of ground cover. In this paper, we describe and apply three complementary shape measures to classify morphologically different waterbodies in a Landsat image. Image segmentation was used to create objects of image pixels containing water, and shape measures were calculated for all obtained objects. A shape-based, a spectra-based and a combined spatial-spectral classification were carried out on a subset of the image using endmembers acquired outside the subset. The spectral classification was based on Euclidean distance. The shape-based and combined spectral-shape classification were based on vector angle, as the chosen shape measures are influenced by the image lattice and could only be used as a relative measure. The results of this approach are discussed and compared to an expert interpretation of the same dataset. Results show that shape measures are affected by image resolution and should be used as a relative measure when objects consist of 500 pixels or less. Although the combined spectral-shape classification was not satisfactory and needs more research, the classification that is solely based on shape measures can distinguish spectrally identical waterbodies and had a score of 94% compared to the expert classification.