Cardiac arrhythmia detection in heart activity related signals, like ECG, has been a center of attention for many researchers in medical field, and is as relevant, if not more, to this day. Aim of this paper is to investigate the accuracy of commonly used classifiers in classifying ECG signal segments of one heart beat into three classes ? normal, unrecognizable and arrhythmia ? and provide insight, which type of, or even specific classifier has a tendency to perform the best under given scenario. Results of the experiments showed that best performing classifiers are instance-based learning algorithms, top two performers being K* algorithm, based on entropic distance measure, with 99% correlation and 3,5% relative absolute error, while testing all input data as test data, 90% and 20% respectively, when testing with 75% of input data as training set, and the rest as testing set, along with IBk ? nearest neighbor based algorithm, which was only applicable with percentage split training method (75% / 25%), resulting in 84% correlation and 19% relative absolute error.