We present here a neural network based method for prediction of amino-terminal acetylation - by far the most abundant post-translational modification in eukaryotes. The method was developed on a yeast data set for N-acetyltransferase A (NatA) acetylation, which is the type of N-acetylation for which most examples are known and for which orthologs have been found in several eukaryotes. We obtain correlation coefficients close to 0.7 on yeast data and a sensitivity up to 74% on mammalian data, suggesting that the method is valid for eukaryotic NatA orthologs.