This paper focuses on identification of an effective pattern recognition scheme with the least number of time domain features for dexterous control of prosthetic hand to recognize the various finger movements from surface electromyogram (EMG) signals. Eight channels EMG from 8 able-bodied subjects for 15 individuals and combined finger activities have been considered in this work. In this work, an attempt has been made to recognize a number of classes with the least number of features. Therefore, EMG signals are pre-processed using dual tree complex wavelet transform to improve the discriminating capability of features and time domain features such as zero crossing, slope sign change, mean absolute value, and waveform length is extracted from the pre-processed data. The performance of extracted features is studied with different classifiers such as linear discriminant analysis, naive Bayes classifier, quadratic support vector machine and cubic support vector machine with and without feature selection algorithms. The feature selection has been studied using particle swarm optimization (PSO) and ant colony optimization (ACO) with different number of features to identify the effect of features. The results demonstrated that naive Bayes classifier with ant colony optimization shows an average classification accuracy of 88.89% with a response time of 0.058025 ms for recognizing the 15 different finger movements with 16 features with significant difference in accuracy compared to SVM classifier with feature selection for a significance level of 0.05. There is no significant difference in the accuracy, specificity and sensitivity of an SVM classifier with and without feature selection. But the processing time is significantly more than the LDA and NB classifier. The PSO and ACO results revealed that slope sign changes contribute to recognizing the activity. In PSO, mean absolute value has been found to be effective compared to waveform length, contradictory with ACO. Further, the zero crossings have been found to be not effective in classification of finger movements in both the methods. © 2018, Australasian College of Physical Scientists and Engineers in Medicine.