This paper presents a novel method for classification of multilead electrocardiogram (ECG) signals. The feature extraction is based on the random projection (RP) concept for dimensionality reduction. Furthermore, the classification is performed by a neuro-fuzzy classifier. Such a model can be easily implemented on portable systems for practical applications in both health monitoring and diagnostic purposes. Moreover, the RP implementation on portable systems is very challenging featuring both energy efficiency and feasibility. The proposed method is tested on a 12-lead ECG database consisting of 20 beats during normal sinus rhythm, 20 beats with myocardial infarction and 20 beats showing cardiomyopathy for 60 different subjects. The experiments give a recognition rate of 100% for a small number of RP coefficients (only 25), i.e. after a considerable dimensionality reduction of the input ECG signal. The results are very promising, not only from the classification performance point of view, but also while targeting a low-complexity feature extraction in terms of computation requirements and memory usage for real-time operation on a wireless wearable sensor platform.