Recent developments in the analysis of functional MRI data reveal a shift from hypothesis-driven statistical tests to unsupervised strategies. One of the most promising approaches is the fuzzy clustering algorithm (FCA), whose potential to detect activation patterns has already been demonstrated. But the FCA suffers from three drawbacks: first the computational complexity, second the higher sensitivity to noise and third the dependence on the random initialization. With the multiresolution approach presented here, these weak points are significantly improved, as is demonstrated in our tests with simulated and real functional MRI data.