The paper presents a fuzzy evidential reasoning algorithm in light of the Dempster-Shafer evidence theory and the K-nearest neighbor algorithm for pattern classification. Given an input pattern to be classified, each of its K nearest neighbors is viewed as an evidence source, in terms of a fuzzy evidence structure. The distance between the input pattern and each of its K nearest neighbors is used for mass determination while the contextual information of the nearest neighbor in the training sample space is formulated by a fuzzy set in determining a fuzzy focal element. Therefore, pooling evidence provided by neighbors is realized by a fuzzy evidential reasoning, where feature selection is further considered through ranking and adaptive combination of neighbors. A fast implementation scheme of the fuzzy evidential reasoning is also developed. Experimental results of classifying multi-channel remote sensing images have shown that the proposed approach outperforms the K-nearest neighbor (K-NN) algorithm [1], the fuzzy K-nearest neighbor (F-KNN) algorithm [2], the evidence-theoretic K-nearest neighbor (E-KNN) algorithm [3], and the fuzzy ex-tended version of E-KNN (FE-KNN) [4], in terms of the classification accuracy and insensitivity to the number K of nearest neighbors.