Summary: A new Bayesian method was proposed to obtain the optimal solution when the prior knowledge was given as a target factor pattern matrix. This method (Bayesian Procrustes Solution) chooses the best target matrix in terms of the posterior probability or Bayes factors when there are two or more possible target matrices. Then the posterior distributions of the relevant parameters are approximated by sufficiently large samples of parameters simulated by the Gibbs sampler. The estimates of the parameters are given as the means of these distributions. A simulation study demonstrated that this method is more robust with respect to misspecification of the target matrix than the traditional method. Finally, the application of this method to real data showed the validity of this method.