In the recent years, Support Vector machines (SVMs) classifiers have been successfully applied to solve a large number of problems. Since SVMs use the global learning technique it suffers from the catastrophic forgetting phenomenon (also called unlearning) which is the inability of the system to learn new patterns without forgetting previously learned ones. Learn++ have been recently introduced as an incremental learning algorithm capable of learning additional data that may later become available. To deal with the catastrophic forgetting problem and to add the incremental learning capability to SVMs classifiers, we propose to use the SVM ensemble with Learn++. Various simulation results for three real world applications that the proposed SVM ensemble show promising results.