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This paper proposes a novel, type-2 fuzzy wavelet neural network (type-2 FWNN) structure that combines the advantages of type-2 fuzzy systems and wavelet neural networks for identification and control of nonlinear uncertain systems. The proposed network is constructed on the base of a set of fuzzy rules that includes type-2 fuzzy sets in the antecedent part and wavelet functions in the consequent part. For structure identification, a fuzzy clustering algorithm is implemented to generate the rules automatically and for parameter identification the gradient learning algorithm is used. The effectiveness of the proposed system is evaluated for identification and control problems of time-invariant and time-varying systems. The results obtained are compared with those obtained by the use of type-1 FWNN based systems and other similar studies.