Impulse noise contamination can affect the interpretability of the magnetic resonance (MR) images. Nonlinear adaptive techniques are often computationally expensive in reducing the noise while retaining the image details. Due to their lack of adaptability, the median filters do not always perform well when the noise probability is relatively high. To provide simplicity and adaptability, here we present a fuzzy weighted mean (FWM) filter that uses both numerical data and linguistic information. The FWM filter determines the weight for each pixel in the neighborhood in response to the local features. In this study, the training data were calculated from twenty impulse noise-free MR images obtained from different regions in humans. A 5 x 5 window was used to scan across the images. The fuzzy system was constructed using the learning from example method and was then merged with Takagi-Sugeno fuzzy system based on information obtained from experts using a one rule-base merging method. Preliminary assessment of the method on twenty noisy images showed encouraging results in effectively reducing the error in the sense of mean square (compare to median filters) and preserving edges and small structures, although the appearance of the original images was not always faithfully recovered.