Heart rate patterns may contain diagnostic as well as forensic information. To test these possibilities, individual heart rate patterns were represented as heart-rate cepstral vectors (HRCVs) computed in 12 dimensions via linear predictive coding (LPC) of brief segments of heart rate. A library of codebook vectors was computed for 12 cardiac patients from a standard ECG database. Statistical classification of subjects was based on the minimal weighted distances between test and codebook vectors. Weights were based on the ratio of inter- to intrasubject variances of their cepstral coefficients. Results showed that: (1) HRCV coefficients adequately reproduced the HRV spectrum, and (2) HRCV distances could be used to identify individuals within the group with a reliability of 93%. Thus, heart rate variations are an individual characteristic that can be represented as a single 12-dimensional vector.