Illustrates the use of visualisation techniques in the analysis of recordings of muscle activity. Surface electromyogram (EMG) and mechanomyogram (MMG) signals were recorded simultaneously, along with muscle force from the quadriceps of 10 able-bodied subjects during isometric exercise. The MMG signal records muscle vibrations via a small accelerometer attached to the skin surface over the muscle belly and the EMG is recorded using standard electrodes attached to the skin over the muscle. The signals were recorded over four seconds for each of 11 levels of muscle force ranging from 0 to 100% maximum voluntary contraction. Model order estimation methods provide us with an insight into the number of processes involved in the generation of complicated signals. They also provide a means of assessing the suitable dimensionality of input data for subsequent classification methods. The visualisation method discussed in this paper is based on unsupervised learning, i.e. the target labels of the input data are not used during training. Although they provide an indication of the likely success of classification methods, it should be commented that the reduction from a high dimensional space to a lower dimension will generally incur a loss of information. Therefore, classification techniques which make full use of the higher dimensional space, such as radial basis functions, generally give better results than those based purely on the output positions of the Kohonen map. (6 pages)