We present an appearance-based linear regression method for pose estimation from a single image of an asteroid, which can have any pose in the full space of three degree-of-freedom rotation parameters. The method is characterized by its division of the parameter space into multiple regions. Given a large number of training images with known pose parameters, we learn the relationship between the images and the pose parameters, separately for each parameter region, using the standard linear pose estimation. We also create a common subspace such that, when projected to it, the difference between images in the same parameter region tends to collapse. In estimating the pose of an input image, we project it onto the common subspace to determine the parameter region. We apply the method for pose estimation from asteroid images and report the experimental results.