An ensemble learning framework is proposed to optimize the receiver operating characteristic (ROC) curve corresponding to a given classifier. The proposed ensemble maximal figure-ofmerit (E-MFoM) learning framework meets four key requirements desirable for ROC optimization, namely: (1) each classifier in the ensemble can be learned with any specified performance metric for any given classifier design; (2) such a classifier is discriminative in nature and attempts to optimize a particular operating point on the ROC curve of the classifier; (3) an ensemble approximation to the overall behavior of the ROC curve can be established by sampling a set of operating points; and (4) ensemble decision rules can be formulated by grouping these sampled classifiers with a uniform scoring function. We evaluate the proposed framework using 3 testing databases, the Reuters and two UCI sets. Our experimental results clearly show that E-MFoM learning outperforms the state-of-the-art algorithms using Wilcoxon-Mann-Whitney rank statistics.