A number of methods for temporal alignment, feature extraction and clustering of electrocardiographic signals are proposed. The ultimate aim of the paper is to find a method to automatically reduce the quantity of beats to examine in a long-term electrocardiographic signal, known as Holter signal, without loss of valuable information for the diagnosis. These signals include thousands of beats and therefore visual inspection is difficult and cumbersome. All the elements involved in each stage will be described and a thorough experimental study will be presented. The electrocardiograph signals used in the experiments belong to the well-known MIT database, where many different waveforms can be found. Based on the results of the experiments, a complete process is proposed to obtain the significant beats present within a signal, with a reasonable computational cost. Hence, cardiologists will only have to examine a small but fully representative subset of beats, making this method a very useful tool for medical decision support systems.