Electromyogram pattern recognition (EMG-PR) based control is a potential method capable of providing intuitively dexterous control functions in upper limb prostheses. Meanwhile, the feature extraction method adopted in EMG-PR based control is considered as an important factor that influences the performance of the prostheses. By exploiting the limitations of the existing feature extraction methods, this study proposed a new feature extraction method to effectively characterize EMG signal patterns associated with different limb movement intent. The performance of the proposed 2-dimensional novel time-domain feature set (NTDFS) was investigated using classification accuracy and feature space separability metrics across five subjects' EMG recordings, and compared with four different existing methods. In comparison to four other previously proposed feature extraction methods, the NTDFS achieved significantly better performance with increment in accuracy in the range of 5.20% ∼ 8.40% at p<0.05. Additionally, by applying principal component analysis (PCA) technique, the PCA feature space for NTDFS show obvious class separability in comparison to the other existing feature extraction methods. Thus, the proposed NTDFS may facilitate the development of accurate and robust clinically viable EMG-PR based prostheses.