The use of bagging is explored to create an ensemble of fuzzy classifiers. The learning algorithm used was ANFIS (adaptive neuro-fuzzy inference systems). We compare results from bagging to those of a single classifier using both crisp and fuzzy classifier combination methods. Results on 20 data sets show that bagging results in a significantly more accurate classifier.