In this paper a new real-time model based pose determination system based on the Gauss-Newton method is described. Various model-fitting strategies, which use gradient search, are discussed and compared. A novel strategy, the adaptive perpendicular search, is proposed for both, matching ambiguity avoidance and convergence enforcement. It is shown to be highly effective in practice, while keeping the computational cost low. A sufficient initial pose estimate as an input for the Gauss-Newton based methods is obtained from the Parametric Eigenspace. With certain restrictions, the overall pose determination system performs in real-time, as required in industrial applications.