Proposes a class of multivariate Tobit models with a factor structure on the covariance matrix. Such models are useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious representation of the censored data. Models are estimated with simulated maximum likelihood. Discusses applications. (SLD)