We present a systematic approach for detecting nonlinear components in heart rate variability (HRV). The analysis is based on twenty-three 48-h Holter recordings in healthy persons during sinus rhythm. Although many segments of 1,024 R-R intervals are stationary, only few stationary segments of 8,192-32,768 R-R intervals can be found using a test of Isliker and Kurths (Int. J. Bifurcation Chaos 3:1573-1579, 1993.). By comparing the correlation integrals from these segments and corresponding surrogate data sets, we reject the null hypothesis that these time series are realization of linear processes. On the basis of a test statistic exploring the differences of consecutive R-R intervals, we reject the hypothesis that the R-R intervals represent a static transformation of a linear process using optimized surrogate data. Furthermore, time irreversibility of the heartbeat data is demonstrated. We interpret these results as a strong evidence for nonlinear components in HRV. Thus R-R intervals from healthy persons contain more information than can be extracted by linear analysis in the time and frequency domain.