In this work, an attempt has been made to investigate the effectiveness of binary bat algorithm as a feature selection method to classify sEMG signals under fatigue and nonfatigue conditions. The sEMG signals are recorded from the biceps brachii muscle of 50 healthy volunteers. The signals are preprocessed and then multiscale Renyi entropy based feature are extracted. The binary bat algorithm is used for feature selection and the effectiveness is compared with information gain based ranker. The performance of the feature selection algorithms are validated by performing classification using Naïve Bayes, and least square support vector machines. The results show a decreasing trend in the multiscale Renyi entropy with increase in scale. Additionally, higher entropy values where observed in fatigue condition. The classification results showed that a maximum accuracy of 86.66% is obtained with least square SVM and binary bat algorithm. It appears that, this technique is useful in identifying muscle fatigue in varied clinical conditions. © Springer International Publishing Switzerland 2015.