Surface analysis methods such as Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) have become essential for probing surfaces and interfaces that are critical determinants of the properties of a diverse range of materials. These methods generate copious amounts of information but this is rarely analyzed by modern artificial intelligence and machine learning methods. Here we calculate the information content of ToF-SIMS spectra, and how this changes with variation in the size of the bins into which the spectra can be assigned. We find that Shannon entropy of the spectra of 10 diverse polymers correlates well with molecular information content of the monomers from which the polymers derive. Surprisingly, we find that most of the information in ToF-SIMS spectra resides in resolutions (bin sizes) of 0.02-1 m/z. At very small bin sizes the information content of the spectra is close to that expected for mass spectral information uniformly distributed across bins. Conversely, for large bin sizes we find that the information content of the spectra is close to that expected for mass spectral information randomly distributed across bins.