—Word Confidence Estimation (WCE) is the task of predicting the correct and incorrect words in the MT output. Dealing with this problem, this paper proposes some ideas to build a binary estimator and then enhance its prediction capability. We integrate a number of features of various types (system-based, lexical, syntactic and semantic) into the conventional feature set, to build our classifier. After the experiment with all features, we deploy a " Feature Selection " strategy to filter the best performing ones. Next, we propose a method that combines multiple " weak " classifiers to build a strong " composite " classifier by taking advantage of their complementarity. Experimental results show that our propositions helped to achieve a better performance in term of F-score. Finally, we test whether WCE output can play any role in improving the sentence level confidence estimation system.