Ensemble learning is apopular and intensively studied field inmachine learning and pattern recognition to increase the performance of the classification. Randomforest is so important for giving fast and effective results. On the otherhand, Rotation Forest can get better performance than Random Forest. In thisstudy, we present a meta-ensembleclassifier, called Random Rotation Forest to utilize and combine the advantagesof two classifiers (e.g. Rotation Forest and Random Forest). In theexperimental studies, we use three base learners (namely, J48, REPTree, and Random Forest) and two meta-learners (namely, Bagging and Rotation Forest)for ensemble classification on five datasets in UCI Machine Learning Repository.The experimental results indicate that Random Rotation Forest gives promisingresults according to base learners and bagging ensemble approaches in terms ofaccuracy rates, AUC, precision and recall values. Our method can be used forimage/pattern recognition and machine learning problems.