Embryonic stem cells (ESCs) are of immense interest as they can proliferate indefinitely in vitro and give rise to any adult cell type, serving as a potentially unlimited source for tissue replacement in regenerative medicine. Extensive analyses of numerous human and mouse ESC lines have shown generic similarities and differences at both the transcriptional and functional level. However, comprehensive proteome analyses are missing or are restricted to mouse ESCs. Here we have used an extensive proteomic approach to search for ESC-specific proteins by analyzing the differential protein expression profiles of human and mouse ESCs and their differentiated derivatives. The data sets comprise 1,775 non-redundant proteins identified in human ESCs, 1,532 in differentiated human ESCs, 1,871 in mouse ESCs, and 1,552 in differentiated mouse ESCs with a false positive rate of <0.2%. Comparison of the data sets distinguished 191 proteins exclusively identified in both human and mouse ESCs but not in their differentiated derivatives. Besides well known ESC benchmarks, this subset included many uncharacterized proteins, some of which may be novel ESC-specific markers. To complement the mass spectrometric approach, differential expression of a selection of these proteins was confirmed by Western blotting, immunofluorescence confocal microscopy, and fluorescence-activated cell sorting. Additionally two other independently isolated and cultured human ESC lines as well as their differentiated derivatives were monitored for differential expression of selected proteins. Some of these proteins were identified exclusively in ESCs of all three human lines and may thus serve as generic ESC markers. Our wide scale proteomic approach enabled us to screen thousands of proteins rapidly and select putative ESC-associated proteins for further analysis. Validation by three independent conventional protein analysis techniques shows that our methodology is robust, provides an excellent tool to characterize ESCs at the protein level, and may disclose novel ESC-specific benchmarks.