Cardiac autonomic neuropathy (CAN) in diabetes has been called a 'silent killer', because so few patients realize that they suffer from it, and yet its effect can be lethal. Early sub clinical detection of CAN and intervention are of prime importance for risk stratification in preventing sudden death due to silent myocardial infarction. This study presents the usefulness of heart rate variability (HRV) and complexity analyses from short term ECG recordings as a screening tool for CAN. Poincaré plot indexes and sample entropy (SampEn) measure of HRV were used for analyzing variability (short and long term) and complexity respectively. Analyses were performed on the different length of HRV records during supine rest. Reduced Poincaré plot patterns and higher SampEn were found in CAN+ group. Significant changes in HRV parameters of CAN+ group during the course of supine rest were found in contrast to control group (CAN-). Our results demonstrated the potential utility of nonlinear HRV parameters in identifying asymptomatic CAN.