This paper describes a selective generate-and-test algorithm for SIMD-parallel calculation of the 3-D pose of a known object from a single perspective view. The algorithm consists of three main stages: object pose estimation, optimization, and reliability analysis. The first stage involves parallel generate-and-test of candidate pose solutions obtained by selectively matching model and image point triples, testing their correspondence by parallel transformation of all visible model points and comparison of their features with the image. Instead of exact algebraic calculations, the three point perspective pose estimation problem is solved numerically. In the second stage, the initial pose estimate is optimized using a least-squares method. In the final stage, the reliability of the optimized pose is estimated using covariance analysis. The simulations showed that the approximate initial pose estimates are sufficiently good to obtain very accurate optimized results. Furthermore, the processing times for an object with 12 vertices were on the order of a few hundreds of milliseconds on a Connection Machine-2 with 512 floating point units.