Feature encoding is a crucial step in BOW image representation. The standard BOW model assigns each image feature to the nearest visual-word without making a distinction between the features that are assigned to the same words. This hard feature assignment leads to high quantization errors and degrades the learning capacity of the classifiers in image classification. We propose a fuzzy feature encoding approach to overcome the uncertainty problem in BOW through assigning each image feature to the visual-words with some membership degrees. We employ two classification techniques, Naive Bayesian and SVM, to evaluate the effect of the fuzzy assignment in image classification. Experiments conducted on image datasets show that fuzzy feature encoding significantly improves the classification accuracy.