The purpose of this study was to examine the recovery of item parameters in simulated Automatic Item Generation (AIG) conditions, using Markov chain Monte Carlo (MCMC) estimation methods to attempt to recover the generating distributions. To do this, variability in item and ability parameters was manipulated. Realistic AIG conditions were simulated, and the SCORIGHT computer program was used to estimate item parameters and simulee ability. There were indications that the MCMC estimation failed to converge in the 2000 cycle run. Histograms for some of the items show that the MCMC procedure had not yet converged for the individual runs or that the program was not operating correctly, and that the former was more likely. It was uncertain that valid inferences would be made based on the analyses. Follow-up work is planned, using 25,000 iterations. (SLD)