Glycomics helps investigate the role glycosylation plays in complex diseases. Liquid chromatography (LC) coupled with mass spectrometry (MS) is routinely used to profile the glycans released from proteins in a biological sample. This enables us to compare observed glycans and their abundances among different biological samples to discover candidate biomarkers. One of the challenges in label-free LC/MS-based glycan profiling is the presence of various charge states and derived adduct ions. We propose a novel Glycan Profile Annotation (GPA) algorithm to automatically cluster and annotate these ions using a graphical model. Specifically, GPA aims to generate a list of unique neutral masses representing putative glycan composition derived from various charge states and multiple adducts. We demonstrate the performance of GPA in recognizing ions derived from the same glycan through analysis of LC/MS data from a serum biomarker discovery study. In addition, a simulation study is carried out to evaluate GPA's performance against existing tools in handling ambiguous cases.