We propose the use of Genetic Programming (GP) to generate new features to predict localised muscles fatigue from pre-filtered surface EMG signals. In a training phase, GP evolves programs with multiple components. One component analyses statistical features extracted from EMG to divide the signals into blocks. The blocks’ labels are decided based on the number of zero crossings. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is applied to group similar data blocks. Each cluster is then labelled into one of three types (Fatigue, Transition-to-Fatigue and Non-Fatigue) according to the dominant label among its members. Once a program is evolved that achieves good classification, it can be used on unseen signals without requiring any further evolution. During normal operation the data are again divided into blocks by the first component of the program. The blocks are again projected onto a two-dimensional Euclidean space by the two other components of the program. Finally blocks are labelled according to the k-nearest neighbours. The system alerts the user of possible approaching fatigue once it detects a Transition-to-Fatigue. In experimentation with the proposed technique, the system provides very encouraging results.