Automated image matching has important applications, not only in the fields of machine vision and general pattern recognition, but also in modern diagnostic and therapeutic medical imaging. Image matching, including the recognition of objects within images as well as the combination of images that represent the same object or process using different descriptive parameters, is particularly important when complementary physiological and anatomical images, obtained with different imaging modalities, are to be combined. Correlation analysis offers a powerful technique for the computation of translational, rotational and scaling differences between the image data sets, and for the detection of objects or patterns within an image. Current correlation-based approaches do not efficiently deal with the coupling of the registration variables, and thus yield iterative and computationally-expensive algorithms. A new approach is presented which improves on previous solutions. In this new approach, the registration variables are de-coupled, resulting in a much less computationally expensive algorithm. The performance of the new technique is demonstrated in the matching of MRI and PET scans, and in an application of pattern recognition in linear accelerator images.