Considering the important advances in treating specific types of systemic amyloidoses, unequivocal typing of amyloid deposits is now essential. Subcutaneous abdominal fat aspiration is the easiest, most common diagnostic procedure. We developed a novel, automated approach, based on Multidimensional Protein Identification Technology, for typing amyloidosis. Fat aspirates were obtained from patients with the most common systemic amyloidoses (ALλ, ALκ, transthyretin, and reactive amyloidosis), with Congo red score more than or equal to 3+, and nonaffected controls. Peptides from extracted and digested proteins were analyzed by Multidimensional Protein Identification Technology. On semiquantitative differential analysis (patients vs controls) of mass spectrometry data, specific proteins up-represented in patients were identified and used as deposit biomarkers. An algorithm was developed to classify patients according to type and abundance of amyloidogenic proteins in samples; in all cases, proteomic characterization was concordant with fibril identification by immunoelectron microscopy and consistent with clinical presentation. Our approach allows reliable amyloid classification using readily available fat aspirates.