We propose a method to determine the amount of
measurement noise present in a chaotic time series. If the data
is embedded in a space of higher dimension than strictly
required to reconstruct the dynamics, the extra dimensions are
dominated by the noise which results in a certain shape of the correlation
integral. For the case that only Gaussian noise is present, this
shape can be calculated analytically as a function of the noise level.
Thus the noise level can be obtained from a simple function fit.
The analytical result also shows that a noise level of more than 2% will 
obscure any possible scaling of the correlation integral and thus makes it 
impossible to estimate the correlation dimension.