This paper discusses the application and performance of multidimensional pattern classification problems using Michigan approach based on fuzzy genetics-based machine learning mechanism, and proposes a new approach. In the approach, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to it. The approach not only retrieves fuzzy if-then rules, but also tunes the membership functions of each dimension, meanwhile the selection mechanism based on the similarity of individuals is involved to reduce the high selective pressure, keep the diversity of population, and avoid the premature convergence problem consequently. Finally the experiments prove that the approach has a better correct classification rate and a better adaptability on multidimensional pattern classification problems.