Top-down proteomics approaches are becoming ever more popular, due to the advantages offered by knowledge of the intact protein mass in correctly identifying the various proteoforms that potentially arise due to point mutation, alternative splicing, post-translational modifications, etc. Usually, the average mass is used in this context; however, it is known that this can fluctuate significantly due to both natural and technical causes. Ideally, one would prefer to use the monoisotopic precursor mass, but this falls below the detection limit for all but the smallest proteins. Methods which predict the monoisotopic mass based on the average mass, are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models, which allows prediction of the monoisotopic mass based on the exact mass of the most abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes. This linear model was tested experimentally as well as in silico, and typically predicts monoisotopic masses with an accuracy of only a few parts per million. A confidence measure is associated to the predicted monoisotopic mass to handle the off-by-one-Da prediction error. Furthermore, we introduce a correction function to extract the i.e. theoretically) most abundant isotope peak from a spectrum, even if the observed isotope distribution is