To enable the efficient comparison of striation marks in forensic science, tools for the automated detection of similarities between them are necessary. Such marks show a groove-like texture which can be considered as a fingerprint of the associated tool. Thus, a reliable detection of connections between different toolmarks from the identical tool can be established. In order to avoid the time-consuming visual inspection of toolmarks, automated approaches for the evaluation of marks are essential. Such approaches are commonly based on meaningful characteristics extracted from images of the marks that are to be examined. Besides of a high recognition rate, the required computation time plays an important role within the design of an adequate comparison strategy. The cross-correlation function presented in this paper provides a faithful quantitative measure to determine the degree of similarity. It is shown that appropriate modeling of the signal characteristics considerably improves the performance of methods based on the cross-correlation function. A strategy for quantitative assessment of comparison strategies is introduced. It is based on the processing of a test archive of marks and analyses the comparison results statistically. For a convenient description of the assessment results, meaningful index numbers are discussed.