Quantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics difficult to interpret biologically. We therefore developed a method, Lobe Contribution Elliptical Fourier Analysis (LOCO-EFA), which generates from digitalised 2D cell outlines meaningful descriptors that can be directly matched to morphological features. This is shown by studying well-defined geometric shapes as well as actual biological cells from plant and animal tissues. LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells, here demonstrated by applying it to the complex shaped pavement cells ofArabidopsis thalianawild type andspeechlessleaves, andDrosophilaamnioserosa cells. To validate our method's applicability on large populations, we analysed computer-generated tissues. By controllingin silicocell shape, we explored the potential impact of cell packing on individual cell shape, quantifying through LOCO-EFA deviations between the specified shape of single cells in isolation and the resultant shape when they interact within a confluent tissue.