Principal component analysis has long been used for a variety of signal processing applications, including signal compression. Neural network implementations of principal component analysis provide a means for unsupervised feature discovery and dimension reduction. In this paper, we describe a method for the compression of ECG data using principal component analysis. Hebbian neural networks were used for principal components computation. A variety of examples of normal and pathological ECGs obtained from the MIT ECG database demonstrate that the proposed method can provide compression ratio up to 30 with PRD% less than 5%.