This paper describes an approach using information theory to derive a complexity measure for proteomics maps generated using 2-dimensional gel electrophoresis (2DE gel). The maps used in this study were partitioned into 5×5 grids and total protein abundance in each grid square was compared to the total abundance for the entire map. Next, Shannon's relation was applied to characterize the distribution of protein abundance across the entire map. Details of the approach are discussed, including an example of the calculations for one proteomics map containing 200 spots. Finally, results for the Map Information Content index are presented for a set of six maps calculated using 200, 500, and 1,400 protein spots. It is hoped that the application of information-theoretic techniques to characterize the complexity of these maps, thus reducing the amount of information presented to the researcher, will help in comparing maps containing a great deal of information and yield information useful in computational toxicology.