Chemical cross-linking is a useful method for deriving information on protein structure and protein-protein interaction. We have developed a method combining chemical cross-linking with mass-spectrometry and bioinformatics (CrossWork) to automate search and validation of cross-links in large-scale experiments. Glycoproteins however have proposed a special challenge to the method, since the glycan moiety of any glycosylated residue tends to be heterogeneous within the same sample, which vastly complicates the search for cross-links . Here we present a new software application, GLYCANthrope, which automates the identification of both the glycopeptides and their N-linked glycosylation(s) from standard MS2 scans of glycoproteins. We have tested the efficiency of GLYCANthrope by searching MS2 data (CID mode) from 6 tryptically digested glycoproteins with a total of 11 known N-linked glycosylation sites, and observed that the glyco-peptides were identified correctly in 11/11 cases and the glycan moieties were annotated in 11/11 cases. Finally, glycan structures were proposed in 10/11 cases, all of which were in agreement with previously reported structures. As a stand-alone program, GLYCANthrope is a useful tool for the identification of N-linked glycosylations, and combined with Crosswork further facilitates cross-linking experiments on glycoproteins.