Abstract : We tackle the issue of multiview learning which aims to take advantages of multiple represen-tations/views of the data. In this context, many machine learning algorithms exist. However, the majority of the theoretical studies focus on learning with exactly two representations. In this paper, we propose a general PAC-Bayesian theory for multiview learning with more than two views. We focus our study to binary classification models that take the form of a majority vote. We derive PAC-Bayesian generalization bounds allowing to consider different relations between empirical and true risks by taking into account a notion of diversity of the voters and views, and that can be naturally extended to semi-supervised learning.