Restricted Boltzmann machines (RBMs) have been proven to be powerful tools in many specific applications, such as representational learning and document modelling. However, the extensions of RBMs are rarely used in the field of multi-view learning. In this paper, we present a new multi-view RBM model, named as the RBM with posterior consistency, for multi-view classification. The RBM with posterior consistency computes multiple representations by regularizing the marginal likelihood function with the consistency among representations from different views. Contrasting with existing multi-view classification methods, such as multi-view Gaussian pro-cess with posterior consistency (MvGP) and consensus and complementarity based maximum entropy discrimination (MED-2C), the RBM with posterior consistency have achieved satisfactory results on two-class and multi-class classification datasets.