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In this study, a novel “SMS spam message filter” utilizing effective feature selection and pattern classification techniques is proposed. The proposed filter detects and filters out SMS spam messages in a smart manner rather than black/white list approaches that require intervention of phone users. In the study, Gini index based approach is preferred as the feature selection method. The feature vectors consisting of the selected discriminative features are then fed into two well-known pattern classifiers, namely Naive Bayes and k-Nearest Neighbor, for recognition process. Furthermore, a mobile application, which exploits the proposed detection scheme, is developed particularly for the mobile phones with Android™ operating system. Thus, SMS spam messages are automatically filtered out without disturbing the phone user. The proposed detection scheme is evaluated on a large SMS message dataset consisting of spam and legitimate messages. The results of the experimental work reveal that the proposed system is considerably successful in filtering SMS spam messages.