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The question whether paleoclimatic systems are governed by a small number of significant variables (low-dimensional systems) is of importance for modeling such systems. As indicators for global Plio-/Pleistocene climate variability, four marine, sedimentary oxygen isotope time series are analyzed with regard to their dimensionality using a modified Grassberger-Procaccia algorithm. An artificial, low-dimensional chaotic time series (Hénon map) is included for the validation of the method. In order to extract equidistant data the raw data are interpolated with the Akima-subspline method since this method minimizes the change in variance due to the interpolation. The nonlinear least-squares Gauss-Marquardt regression method is used instead of the linear least-squares fit to the logarithmically transformed points, in order to acquire an unbiased estimate of the correlation dimension. The dependences of the estimated correlation dimension on the embedding dimension do not indicate a small number (i.e., less than 5) of influencing variables on the investigated paleoclimatic system, whereas the low dimension for the Hénon map is verified (dimension 1.22–1.28). Because of the limited amount of data in the oxygen isotope records, dimensions greater than about 5 cannot be examined.