Correlation is a common and powerful method for updating inertial guidance systems. Performance of correlation methods degrades in the presence of geometric distortion between the images being correlated, or when the image structure is strongly asymmetric. The Multiple Subarea Correlation (MSC) technique has been developed to reduce performance losses due to these effects. The MSC technique consists of selecting a set of subareas from the reference image, and correlating each reference subarea against the sensed image, producing a correlation function for each subarea. There must be at least three subareas; typically six subareas are selected. The correlation functions are processed to determine a consistent set of local maxima which are in gross agreement as to the relative displacement of the two images. Then, using this set of local maxima and the known subarea locations, a least-squared-error estimate of an affine transformation between the two images is computed. The transformation is applied to the update point in the reference image to find the corresponding point in the sensed image. The technique allows selection of subareas with the most favorable content for correlation. Optimum subarea dimensions exist and depend upon the amount of distortion expected. The variance of the update point position is shown to be inversely proportional to the number of subareas.