This technical note addresses the problem of set membership identification of Wiener systems. Its main result shows that even though the problem is generically NP-hard, it can be reduced to a tractable convex optimization through the use of risk-adjusted methods. In addition, this approach allows for efficiently computing worst-case bounds on the identification error. Finally, we provide an analysis of the intrinsic limitations of interpolatory algorithms. These results are illustrated with a non-trivial problem arising in computer vision: tracking a human in a sequence of frames, where the challenge here arises from the changes in appearance undergone by the target and the large number of pixels to be tracked.