The problem of source identification and quantitative mass apportion for airborne particulate matter commonly called receptor modeling can be treated in a manner analogous to the multivariate calibration problem commonly encountered in Ε½ . chemometrics. Partial least-squares PLS has been previously used in such a context. In this work, artificial neural networks Ε½ . Ε½ . ANN and simulated annealing SA have been applied to the sets of simulated data. The aerosol composition data gener-Ε½ . ated by the National Bureau of Standards NBS for the 1982 EPA workshop on mathematical and empirical receptor modeling held at Quail Roost, NC, have been examined. From these tests of ANN and SA and earlier work on partial least-squares, it appears that multivariate calibration methods may be helpful in resolving sources and apportioning the airborne mass. ANN was better able to deal with the collinearity in the source profile matrix. For CMB and PLS, this collinearity prevented the apportionment of mass to all of the known sources. In addition, ANN could identify which sources were active when trained with a source profile library containing more sources than actually contributed to the samples. SA produced more accurate source contribution estimates than the other methods, but was also bothered by the collinearity to the same degree as the CMB or PLS results. Thus, the initial results with these methods are promising, but further development and testing are needed before they can be routinely used.