In simulation studies boosting algorithms seem to be susceptible to noise. This article applies Ada.Boost.M2 used with decision stumps to the digit recognition example, a simulated data set with attribute noise. Although the final model is both simple and complex enough, boosting fails to reach the Bayes error. A detailed analysis shows some characteristics of the boosting trials which influence the lack of fit.