Due to the growing use of human action recognition in every day life applications, it has become one of the very hot topics in image analysis and pattern recognition. This paper presents a new feature extraction method for human action recognition. The method is based on the extraction of Trace transforms from binarized silhouettes, representing different stages of a single action period. A final history template composed from the above transforms, represents the whole sequence containing much of the valuable spatio-temporal information contained in a human action. The new method takes advantage of the natural specifications of the specific Trace transform, such as noise robustness, translation invariance and scalability easiness and produces effective, simple and fast created features. Classification experiments performed on KTH action database using Radial Basis Function (RBF) Kernel SVM, provided very competitive results indicating the potentials of the proposed technique.