Calibrating a network of cameras with non-overlapping views is an important and challenging problem in computer vision. In this paper, we present a novel technique for camera calibration using a planar mirror. We overcome the need for all cameras to see a common calibration object directly by allowing them to see it through a mirror. We use the fact that the mirrored views generate a family of mirrored camera poses that uniquely describe the real camera pose. Our method consists of the following two steps: (1) using standard calibration methods to find the internal and external parameters of a set of mirrored camera poses, (2) estimating the external parameters of the real cameras from their mirrored poses by formulating constraints between them. We demonstrate our method on real and synthetic data for camera clusters with small overlap between the views and non-overlapping views.