Spam is a severe widespread predicament which causes troubles for almost every computer user and as well as for companies and institutions. Detection of spam e-mail is a difficult task due to several complications occurring in the process of exchanging information over a network. In this paper, we have demonstrated an approach for designing a spam filtering technique using ensemble-based bagging technique. The bagging technique classifies the e-mail message into either spam or not spam (i.e., ham). Bagging being an effective classifier is a powerful pattern recognition and machine learning methodology that is widely used in the literature. Our approach is tested for performance with other existing methods. It is observed that our method, i.e., bagging gives the better accuracy while classifying e-mail instances. Experimental outcome also exhibits that the framework using bagging technique, we attain better precision and recall. Choosing the optimal value of the parameters is a crucial criterion, and this was achieved by performing ten fold cross-validations.