this paper: an algorithmic extension of the crisp relevance test, a Bootstrap fuzzy relevance test and an asymptotic fuzzy relevance test. The results of the Bootstrap fuzzy relevance test are very good, but the high computing time makes its application only practicable for a small number of data samples. The asymptotic fuzzy relevance test supplies good results for a higher number of data samples. The algorithmic extension of the crisp relevance test tends to calculate too large condence intervals, but has the smallest computing time. The employment of the three relevance tests will depend on the respective application. For high dimensional search spaces with a multitude of relevant rules, the algorithmic extension is acceptable, especially, if for each input and output variable several trapezium fuzzy sets are reasonable. In the other cases, the higher eoeort of the fuzzy relevance tests can remunerate. The calculation of estimators and condence intervals on fuzzy data is also meaningful for other test and rating strategies, e.g. the results can be directly used for the method 'Condent Normalized Hit Rate' of Jessen and Slawinski [9]. Acknowledgement