Leptomeningeal metastasis (LM) is a devastating complication occurring in 5% of breast cancer patients. Early diagnosis and initiation of treatment are essential to prevent neurological deterioration. However early diagnosis of LM remains challenging because 25% of CSF samples test false negative at first cytological examination. We developed a new, mass spectrometry based method to investigate the protein expression patterns present in the CSF from breast cancer patients with and without LM. CSF samples from 106 patients with active breast cancer (54 with LM and 52 without LM) and 45 controls were digested with trypsin. The resulting peptides were measured by matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS). Then, the mass spectra were analyzed and compared between patient groups using newly developed bioinformatics tools. A total of 895 possible peak positions was detected and 164 of these peaks discriminated between the patient groups (Kruskal-Wallis, p <0.01). The discriminatory masses were clustered and a classifier was built to distinguish breast cancer patients with and without LM. After bootstrap validation, the classifier had a maximum accuracy of 77% with a sensitivity of 79% and a specificity of 76%. Direct MALDI-TOF analysis of tryptic digests of CSF gives reproducible peptide profiles that can assist in diagnosing LM in breast cancer patients. The same method can be used to develop diagnostic assays for other neurological disorders.