We apply a recently proposed nonlinear noise reduction method to
time sequences from two different experiments
(Taylor-Couette flow and NMR-laser chaos).
We demonstrate that it is not difficult to choose the parameters of 
this algorithm, even though we use no other information about the underlying
dynamics than the data itself. The noise reduction is very robust with 
respect to 
changes in the choice of parameters. The reliability of the result
is tested by an analysis of the corrections.
We discuss the effect of noise reduction on estimates 
of dimensions, entropies and Liapunov exponents.
For comparison we process one of the sets, densely sampled
Taylor-Couette flow data, with a global SVD filter.