By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.