Nowadays, the fuzzy c-means method (FCM) became one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called robust weighted fuzzy c-means (RWFCM). We used a new objective function that uses some kinds of weights for reducing the infection of noises in clustering. Experimental results show that compared to three well-known clustering algorithms, namely, the fuzzy possibilistic c-means (FPCM), credibilistic fuzzy c-means (CFCM) and density weighted fuzzy c-means (DWFCM), RWFCM is less sensitive to outlier and noise and has an acceptable computational complexity.