To effectively use automated zoom lenses for machine vision we need camera models that are valid over continuous ranges of lens settings. While camera calibration has been the subject of much research in machine vision and photogrammetry, for the most part the resulting models and calibration techniques have been for cameras with fixed parameter lenses where the lens' imaging process is static. For cameras with automated lenses the image formation process is a dynamic function of the lens control parameters. The complex nature of the relationships between the control parameters and the imaging process plus the need to calibrate them over a continuum of lens settings makes both the modeling and the calibration of cameras with automated zoom lenses fundamentally more difficult than that of cameras with fixed parameter lenses. In this paper we illustrate some of the problems involved with the modeling and calibration of cameras with variable parameter lenses. We then show how an iterative, empirical approach to modeling and calibration can produce a dynamic camera model of perspective projection that holds calibration across a continuous range of zoom.