We describe a computer algorithm for classifying serum monoclonal proteins (MC) based on serum protein electrophoresis (SPE) and the automated measurement of kappa and lambda light chains and IgG, IgA, and IgM. We developed the algorithm by using a large database of unselected samples containing MC collected in a multicenter study. The performance of the algorithm was optimized by using iterative computational procedures and was tested on both the development database and on an independent set of MC-containing samples. With the development database, the algorithm correctly classified 50% and misassigned 2.5% of the MC. Where the MC were present in concentrations greater than 10 g/L, the rate of successful classification increased to 72% with 3% misclassification. When the algorithm was tested on a group of 101 MC-containing samples from an independent source, 67% were correctly classified and 8% misclassified, half of the latter being unusual IgD myelomas. We discuss the scope for the application of the algorithm in routine laboratory practice involving personal computer software.