Linear correlation techniques are useful approaches for template matching. However, they are computationally intensive since large numbers of multiplications are involved in their calculation. This paper introduces a family of rank-order-based criteria (ROBC) which are multiplier free and do not depend on the local average of the image/template. The most primitive member of this family has properties analogous to the properties of the normalized linear correlation. Hence, we call it normalized min-max cross-correlation (NMCC). Experimental results are presented that describe the performance of the introduced criteria in the presence of Gaussian and impulsive noise. These experiments show that the NMCC features sharp and robust indications in the presence of Gaussian noise. Other members of the ROBC family with more rank order terms also are robust with respect to impulsive noise.