A common problem about matching is how to select the best subset according to a given criterion. The key factors of subset selection are uniqueness and lack of ambiguity. Uniqueness could be decided by the number and magnitudes of the peaks in the cross correlation function of the original and subset functions. Lack of ambiguity could be measured by the correlation length or 50 percent width of the main correlation peak. The smaller the correlation length, the greater the probability of detecting the correct matching position. In this paper, a method is presented to select the best subset according to the minimum correlation length. The analysis can be extended to the two , three and higher dimensional problems. In discrete case the problems can be solved by using the fast Fourier transform (FFT) algorithm.