As the use of stochastic frontier (SF) models increases in the various fields of economics and finance, the need to address the problem of measurement errors in variables becomes urgent. In this paper, we propose a generalized method of moment (GMM) estimator for a stochastic frontier model to correct the measurement error problem. The estimator differs from the method of moment (MoM) estimator of Chen and Wang (2004) in two important ways: (1) The GMM estimator is proposed for a SF model with a truncated-normal random variable, which is a more flexible and empirically-popular model than the half-normal SF model targeted by the MoM estimator. (2) The GMM estimator uses more moment conditions and is more efficient. Simulation results show that the GMM estimator performs quite well for data with a reasonable sample size.