A new simple scoring technique is developed in a binary supervised classification context when only a few observations are available. It consists in two steps: in the first one partial scores are obtained, one for each predictor, either categorical or continuous. Each partial score is a discrete variable with 7 values ranging from -3 to 3, based upon an empirical comparison of the distributions for each class. In a second step the partial scores are added and standardised into a global score, which allows a decision rule. This simple technique is successfully compared with classical supervised techniques for a classical benchmark and has been proved to be especially well fitted in an industrial problem.