Summary This paper considers estimation of a transformation model in which the transformed dependent variable is subject to classical measurement error. We consider cases in which the transformation function is known and unspecified. In special cases (e.g. log and square-root transformations), least-squares or non-linear least-squares estimators are applicable. A flexible approximation approach (based on Taylor expansion) is proposed for a parametrized transformation function (like the Box–Cox model), and a semi-parametric approach (combining a semi-parametric linear-index estimator and non-parametric regression) is proposed for the case of an unspecified transformation function. The methods are applied to the estimation of earnings equations, using wage data from the Current Population Survey (CPS).