The registration of various data is a challenging task in medical image processing and a highly frequented area of research. Most of the published approaches tend to fail sporadically on different data sets. This happens due to two major problems. First, local optimization strategies induce a high risk when optimizing nonconvex functions. Second, similarity measures might fail if they are not suitable for the data. Thus, researchers began to combine multiple measures by weighted sums. In this paper, we show severe limitations of such summation approaches. We address both issues by a gradient-based vector optimization algorithm that uses multiple similarity measures. It gathers context information from the iteration process to detect and suppress failing measures. The new approach is evaluated by experiments from the field of 2D-3D registration. Besides its generic character with respect to arbitrary data, the main benefit is a highly robust iteration behavior, where even very poor initial guesses of the transform result in good solutions.