A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization to a model with multidimensional ability parameters are discussed. The procedure is a generalization of a procedure by J. Albert (1992) for estimating the two-parameter normal ogive model. The procedure will support multiple samples from multiple populations and restrictions on the factor matrix for testing specific hypotheses about the ability structure. The technique is illustrated using simulated and real data. A Markov chain Monte Carlo (MCMC) procedure is used to sample the posterior distributions of interest and needed chains are constructed using the Gibbs sampler (A. Gelfand and A. Smith, 1990). (Contains 8 tables and 47 references.) (Author/SLD)