Biomonitoring programs that use mussels to assess the water quality around the world could benefit from the use of proteomics techniques. These could be applied to obtain protein expression signatures of exposure to pollution that could be further used for prediction purposes. This would require that a combination of univariate and multivariate statistical analyses of proteomics data were utilized to obtain robust models. We show an application of this approach on mussels exposed to fresh fuel, and weathered fuel in a laboratory experiment that tried to mimic the effects of the Prestige’s oil spill. By the combination of those statistical analyses, a set of protein spots were selected that could be used to classify mussels exposed to the two types of fuel oil. As an example of the possibilities that this approach could offer to biomonitoring programs, mussels were collected from ten sampling stations along the NW and NE coasts of the Iberian Peninsula, and their protein expression patterns monitored.