In this paper we present the system we submitted to the WMT12 shared task on Quality Estimation. Each translated sentence is given a score between 1 and 5. The score is obtained using several numerical or boolean features calculated according to the source and target sentences. We perform a linear regression of the feature space against scores in the range [1:5]. To this end, we use a SupportVectorMachine. We experimentwith two kernels: linear and radial basis function. In our submission we use the features from the shared task baseline system and our own features. This leads to 66 features. To deal with this large number of features, we propose an in-house feature selection algorithm. Our results show that a lot of information is already present in baseline features, and that our feature selection algorithm discards features which are linearly correlated.