Over the christmas period we heared that our application was awarded by the academy (3V fund of KNAW)! A short project blurp: In many animals boys and girls look quite alike. Especially in birds the genitals are not located on the outside, and there may be little differences in appearance that allow us to distinguish between the sexes. In fact, in bird studies scientists generally rely on taking a tissue sample and perform DNA analysis in the lab to reveal the sex of an individual. We are working on a quicker, cheaper and non-invasive method to separate the boys from the girls using machine learning. We train a machine learning algorithm to use subtle differences in appearance in photos of birds (colour, shape) in combination with additional biometric measures (wing, leg and bill length, body mass) to classify birds from photos into either sex. We train this algorithm with photos of birds of which we have determined the sex in the lab. Subsequently we test how well this algorithm is able to predict the sex in a new dataset of photographed birds. Our aim is to explore how reliable ‘sex machine learning’ is and whether it can be used on a large variety of species using and develop a freely available software tool. This project is in collaboration with Track32, Dr. Bruno Ens (Sovon) and Dr. Henk van der Jeugd (Dutch Centre for Avian Demography).