Plasma protein profiling using separations coupled to matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has great potential in translational research; it can be used for biomarker discovery and contribute to disease diagnosis and therapy. Previously reported biomarker searches have been done solely by MS protein profiling followed by bioinformatics analysis of the data. To add to current methods, we tested an alternative strategy for plasma protein profiling using pancreatic cancer as the model. First, offline solid-phase extraction is done with 96-well plates to fractionate and partially purify the proteins. Then, multiple profiling and identification experiments can be conducted on the same protein fractions because only 5% of the fractions are used for MALDI MS profiling. After MALDI MS analysis, the mass spectra are normalized and subjected to a peak detection algorithm. Over three sets of mass spectra acquired using different instrument variables, approximately 400 unique ion signals were detected. Classification schemes employing as many as eight individual peaks were developed using a training set with 123 members (82 cancer patients) and a blinded validation set with 125 members (57 cancer patients). The sensitivity of the study was 88%, but the specificity was significantly lower, 75%. The reason for the low specificity becomes apparent upon protein identification of the ion signals used for the classification. The identifications reveal only common serum proteins and components of the acute phase response, including serum amyloid A, alpha-1-antitrypsin, alpha-1-antichymotrypsin, and inter-alpha-trypsin inhibitor.