In this paper a copula approach is applied as a tool for assessing the measure of statistical dependence of parallel cardiovascular time series. Families of Archimedean copulas (Clayton, Frank and Gumbel) are applied to pulse interval, systolic and diastolic blood pressure recorded from male Wistar rats at baseline conditions, and to their isodistributional surrogates with the same marginal, but randomized joint distribution functions. The influence of time offset of the parallel time series is explored. The amount of data required for a stable working point is discussed.