This paper presents an alternative approach to existing and widely used correlation metrics through the use of orientation information. The gradient field correlation method presented here utilises derivative of Gaussian (DoG) operators for estimating directional derivatives of an image for two matching applications: classical planar object detection and point correspondence matching. The experimental results confirm that a suitably normalised gradient vector field, which emphasises gradient direction information in an image, leads to better selectivity when applied to classical template matching problems. For the case of establishing point correspondences, combinations of gradient vector field metrics yield higher in lying match percentages (by RANSAC) relative to normalised cross-correlation with little extra computational cost, particularly at smaller patch sizes. It is also shown that pixel-wise field component normalisation is critical to the success of this approach.