Hello Thomas,

I guess part of the problem is that your data are very well described by a single gaussian and therefore the fit may end up in different local minima. One way to steer which part of the data are well approximated by the model function is to set start values for the parameters to be fitted. I prefer to use NonlinearModelFit

nlm = NonlinearModelFit[data, 
  A 1/(\[Sigma] Sqrt[2. \[Pi]])
    Exp[-(1./2.) ((x - \[Mu])/\[Sigma])^2], {{\[Sigma], 100.}, {\[Mu],
     2000}, {A, 200}}, x]

To visually compare data and fitted model you use

Show[ListPlot[data, PlotStyle -> Red, PlotRange -> All], 
 Plot[nlm[x], {x, 0, 2000}, PlotRange -> All]]

Regards, Michael

Thanks a lot!

I was not aware that I can set start values. This information is very helpful :-)

How do the start values influence the approximated values by mathematica? I have about 30 of these datasets where I do not expect sigma to change much but mu varies a lot. The point is, I want my notebook to evaluate the data automatically and I have set up everything to this point in that way. I have a formula to predict where mu should be, but if the start value affects the outcome of mu I'd rather use a different way.