Early detection of subclinical autonomic dysfunction is of vital importance in patients with diabetes mellitus (DM) for the prevention of subsequent serious adverse consequences. Reduction in heart rate variability (HRV) is now regarded as the earliest indicator of cardiovascular dysregulation in DM. HRV has traditionally been quantified using linear measures, which describe the magnitude of RR interval oscillations, but are insufficient to characterize complex heart rate dynamics. While HRV is mostly mediated by parasympathetic nervous system, beat-to-beat blood pressure recordings may provide information regarding sympathetic activity. A variety of novel measures has been developed to quantify nonlinear features of cardiovascular signals, providing information on the complexity of the dynamical system involved in the genesis of these short-term fluctuations. In this book, it is demonstrated that novel nonlinear methods are often more sensitive to autonomic dysregulation than linear methods and therefore may improve the diagnostic power of cardiovascular variability analysis for cardiovascular autonomic neuropathy in DM. Our data indicate that cardiovascular dysregulation progresses in relatively short time frames, depending on the history of DM. Further, its progression appears to be associated with glycemic control. Different methods of cardiovascular variability analysis can provide mutually independent information and therefore should be used simultaneously for a comprehensive analysis of autonomic dysfunction to identify patients at risk for autonomic neuropathy.