This work presents a new approach to the problem of calibrating a zoomable camera. The calibration of zooming cameras is central for tasks which employ zoom to improve feature detection and correspondence, such as 3D stereo reconstruction. Our method solves for the parameters of a camera model using a global optimization technique on a sequence of images of a known calibration target obtained for different mechanical zoom settings. This approach addresses two primary weaknesses in classical camera calibrations. First, the process avoids the difficulties of explicit feature detection. Feature localization is instead included as part of the error measure used in the optimization. Second, images are not calibrated independently, as in previous efforts. Rather, the optimization process considers all images simultaneously, representing the final calibrated camera as a function of zoom. We compute a starting point for the optimization using the measured mechanical zoom settings for the images, and certain features identified by either a high-level process or a human operator. This paper describes the details of our approach, showing initial experimental results on real data.