We present a method to estimate the quality of automatic translations when reference translations are not available. Quality estimation is addressed as a two-step regression problem where multiple features are combined to predict a quality score. Given a set of features, we aim at automatically extracting the variables that better explain translation quality, and use them to predict the quality score. The soundness of our approach is assessed by the encouraging results obtained in an exhaustive experimentation with several feature sets. Moreover, the studied approach is highly-scalable allowing us to employ hundreds of features to predict translation quality.