Abstract: Non-free cysteines that are not involved in the formation of disulfide bridges are very often bound to prosthetic groups that include a metal ion and that play an important role in the function of a protein. The discrimination between the presence of a disulfide bridge (DB) or a metal binding site (MBS) in correspondence of a bound cysteine is often a necessary step during the NMR spectral assignment process of metalloproteins and its automation may significantly help towards speeding up the overall process. Several proteins are known where both situations are in principle plausible and it is not always possible to assign a precise function to each cysteine (see e.g. {2,1,5]). We formulate the prediction task as a binary classification problem: given a non-free cysteine and information about flanking residues, predict whether the cysteine can bind to a prosthetic group containing a metal ion (positive class) or it is always bound to another cysteine forming a disulfide bridge (negative class). Firstly, we suggest a nontrivial baseline predictor based on PROSITE pattern hits. Secondly, we introduce a classifier fed by multiple alignment profiles and based on support vector machines (SVM)[3]. We show that the latter classifier is capable of discovering the large majority of the relevant PROSITE patterns, but is also sensitive to signal in the profile sequence that cannot be detected by regular expressions and therefore outperforms the baseline predictor.