Objective: Cardiovascular complications are the main cause of death in people with diabetes. Early, asymptomatic changes are due to autonomic nervous system dysfunction, which if identified can lead to improved health. This study used detrended fluctuation analysis to identify changes in heart rate variability (HRV) associated with short-time electrocardiograph (ECG) recordings. The aim of the study was to determine whether heart rate variability analysis on short ECG recordings has the potential to be a useful adjunct to clinical practice. Design: Comparative design with three independent simple random samples. Setting: University-based research project. Participants: Forty-eight people with no diabetes or cardiovascular complications had a 20 min ECG recorded, which was subsequently analysed using mathematical procedures. All participants also had a lying-to-standing autonomic nervous system test. Data was analysed using a Student t-test. Results: Heart rate variability expressed as a numeric value (' 1 ), is reduced in disease states. We found a significant difference in ' 1 (P = 0.03) between the ECG recordings of the diabetes and control groups. In addition lower ' 1 values were obtained from people identified with autonomic dysfunction within the diabetes group. Conclusion: The importance of our findings is that abnormal HRV identifies people with cardiovascular disease, irrespective of diabetes status, that may have autonomic neuropathy. HRV analysis is easily implemented by primary health care providers and has the potential to lead to improved health care by reducing inequity in rural areas and specifically addressing cardiovascular complications associated with diabetes.