Correlated binary data arise in many applications. Any analysis of this type of data should take into account the correlation structure among the variables. The multivariate Probit model (MVP), introduced by Ashford and Snowden (1970), is a popular class of models particularly suitable for the analysis of correlated binary data. In this class of models, the response is multivariate, correlated and discrete. Generally speaking, the MVP model assumes that given a set of explanatory variables the multivariate response is an indicator of the event that some unobserved latent variable falls within a certain interval. The latent variable is assumed to arise from a multivariate normal distribution. Difficulties with the multivariate Probit are mainly due to computation as the likelihood of the observed discrete data is obtained by integrating over a multidimensional constrained space of latent variables. In this work, we adopt a Bayesian approach and develop an an efficient Markov chain Monte Carlo algorithm for estimation in MVP models under the full correlation and the structured correlation assumptions. Furthermore, in addition to simulation results, we present an application of our method to the Six Cities data set. Our algorithm has many advantages over previous approaches, namely it handles identifiability and uses a marginally uniform prior on the correlation matrix directly.