We propose a novel feature description method called SHORT (Shell Histograms and Occupancy from Radial Transform) for fast 3D object recognition. In 3D object recognition for point cloud data, it is very important to detect keypoints and describe features rapidly because of the huge amount of data involved. The state-of-the-art keypoint detection methods calculate statistics including covariance matrices from the point cloud in local regions of the object. Then, the state-of-the-art method, which describe features such as normal vector distributions of the point cloud, use all points in the local regions. However, these methods involve high processing costs because they need to calculate the statistics needed for keypoint detection. They also need to use a lot of points in the regions for feature description. By contrast, the SHORT method consists of a fast keypoint detector that does not calculate statistics and a fast feature descriptor that uses only a small number of points in the restricted local regions. The keypoint detector uses occupancy estimated simply like counting the points in regions of outermost shells in spheres, and the feature descriptor uses estimated those and a small number of points including the spherical shell regions of multiple scales. Experimental results in 3D object recognition show that the processing speed of the proposed method is five times faster than that of a comparative method that had a nearly equal 99.4% recognition success rate.