In this paper, we study nonclassical measurement error in the continuous dependent variable of a semiparametric transformation model. The latter is a popular choice in practice nesting various nonlinear duration and censored regression models. The main complication arises because we allow the (additive) measurement error to be correlated with a (continuous) component of the regressors as well as with the true, unobserved dependent variable itself. This problem has not yet been studied in the literature, but we argue that it is relevant for various empirical setups with mismeasured, continuous survey data like earnings or durations. We develop a framework to identify and consistently estimate (up to scale) the parameter vector of the transformation model. Our estimator links a two-step control function approach of Imbens and Newey (2009) with a rank estimator similar to Khan (2001) and is shown to have desirable asymptotic properties. We prove that ‘m out of n’ bootstrap can be used to obtain a consistent approximation of the asymptotic variance and study the estimator’s finite sample performance in a Monte Carlo Simulation. To illustrate the empirical usefulness of our procedure, we estimate an earnings equation model using annual data from the Health and Retirement Study (HRS). We find some evidence for a bias in the coefficients of years of education and age, emphasizing once again the importance to adjust for potential measurement error bias in empirical work.