Point cloud provides a compact and flexible representation for 3D shapes and recently attracts more and more attention due to the increasing demands in practical applications. The major challenge of handling such irregular data is how to achieve the permutation invariance of points in the input. Most of existing methods extract local descriptors that encode the geometry of local structure, followed by a symmetric function to form a global representation. The max pooling usually serves as the symmetric function and shows slight superiority compared to the average pooling. We argue that some discrimination information is inevitably missing when applying the max pooling across all local descriptors. In this paper, we propose the BoW pooling, a plug-and-play unit to substitute the max pooling. Our BoW pooling analyzes the set of local descriptors statistically and generates a histogram that reflects how the primitives in the dictionary constitute the overall geometry. Extensive experiments demonstrate that the proposed Bow pooling is efficient to improve the performance in point cloud classification, shape retrieval and segmentation tasks and outperforms other existing symmetric functions.