Robustness to rotation is critical for point cloud understanding tasks, as point cloud features can be affected dramatically with respect to prevalent rotation changes. In this work, we propose SeLCA, a novel self-supervised learning framework to learn to the canonical axis of point clouds in a probabilistic manner. In essence, we propose to \textit{learn} rotational-equivariance by predicting the canonical axis of point clouds, and achieve rotational-invariance by aligning the point clouds using their predicted canonical axis. When integrated into a rotation-sensitive pipeline, SeLCA achieves competitive performances on the ModelNet40 classification task under unseen rotations. Most interestingly, our proposed method also shows high robustness to various real-world point cloud corruptions presented by the ModelNet40-C dataset, compared to the state-of-the-art rotation-invariant method.