The success of biological signal pattern recognition depends crucially on theselection of relevant features. Across signal and imaging modalities, a largenumber of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that,due to the inherent biological variability, even the same classification problemon different datasets can display variations in the respective optimal sets,casting doubts on the generalizability of relevant features. Here, we approachthis problem by leveraging topological tools to create charts of features spaces.These charts highlight feature sub-groups that encode similar information(and their respective similarities) allowing for a principled and interpretablechoice of features for classification and analysis. Using multiple electro-myographic (EMG) datasets as a case study, we use this feature chartto identify functional groups among 58 state-of-the-art EMG features, and toshow that they generalize across three different forearm EMG datasets obtained from able-bodied subjects during hand and finger contractions.We find that these groups describe meaningful non-redundant information,succinctly recapitulating information about different regions of featurespace. We then recommend representative features from each group basedon maximum class separability, robustness and minimum complexity.