Compared with ordinary regression models, the computational cost for parame ters estimation in general measurement error models is often much more expensive because the estimation procedures typically require solving integral equations.In addition, natural criterion functions are often unavailable for general measurement error models.Because of these difficulties, traditionally-best variable selection procedures are not applicable, and in the measurement error model context, variable selection remains an unsolved issue.In this paper, we develop a novel framework for variable selection in measurement error models via penalized estimating equations.We first propose a class of new selection procedures for general parametric mea surement error models and for general semiparametric measurement error models, and study the asymptotic properties of the proposed procedures.Then, under cer tain regularity conditions and with a properly chosen regularization parameter, we demonstrate that the proposed procedure performs as well as an oracle procedure.We assess the finite sample performance via Monte Carlo simulation studies and illustrate the proposed methodology through the empirical analysis of a real data set.