Abstract : High-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate the automatic classification performances, we tested our algorithm on a significantly large database of cellular images annotated by experts of our group. Results in term of Mean Avarage Precision (MAP) are very promising, providing precision upper than 87% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications. Such supervised classification method has many other applications in cell imaging in the areas of research in basic biology and medicine but also in clinical histology.