In the context of Automatic Modulation Classification (AMC), some recent works have utilized multiple features to train the neural network. With an ultimate aim to develop a systematic approach to select the most diverse and unique features, we propose and demonstrate a novel method to select the most diverse ( ₂ ^m ) features from a larger feature set. Bhattacharyya distance metric for the dissimilarity between two probability distributions is utilized to select the features with the highest distance for all modulation pairs within a test pool. The proposed approach is analyzed for three different neural networks based classifiers, amidst AWGN and frequency-selective fading channels. A substantial reduction in computational complexity is achieved with an acceptable compromise on the classification performance.