A preliminary Markov chain Monte Carlo (MCMC) calibration algorithm is presented for estimating the demand pattern multipliers of a distribution system network model utilizing tracer test data. A simple 35 node distribution system network is used to generate a synthetic tracer study data set over a 55-hour simulation time. The MCMC calibration algorithm is able to reproduce the 55-hour demand pattern multipliers from an initial guess absent of any temporal information. This preliminary study for the MCMC calibration algorithm provides the basis for extending the capabilities to include spatial correlation of user demands. Includes 5 references, figures.