In this paper, a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding is presented. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are developed for calculating the weight in the SWFCM algorithm so as to improve the performance of image thresholding. To speed up the FCM algorithm, the iteration is carried out with the statistical gray level histogram of image instead of the conventional whole data of image. Some comparisons with classical thresholding algorithm and fuzzy thresholding algorithm are also given in this paper. Experimental results on both synthetic and real images are given to demonstrate the effectiveness of the proposed algorithm. In addition, due to the neighborhood model, the proposed method is more tolerant to noise.