Fuzzy C-Means (FCM) clustering has been widely used in remote sensing and computer vision. However, when dealing with multispectral images, the conventional FCM regards spectral responses of all bands on each pixel as a feature vector and conducts image clustering by searching cluster centers in a multi-dimensional space. It is rather time-consuming due to the fact that it has to visit each pixel many rounds during the iteration procedure. Besides, it is sensitive to noise, which mainly results from its ignorance spatial information. In order to overcome these problems, a novel histogram-based fuzzy clustering method is presented in this paper. The proposed method clusters each band independently and fuses the results to form the final segmentation map. On each band, a spatial-spectral... image is computed previously, and then the histogram of this image is exploited to find the initial clusters, which is followed by a clustering procedure directly performed on the histogram instead of image pixels. The experimental results over remote sensing images show that the proposed method can achieve more accurate results but uses less time.