This paper presents initial steps towards an auto-mated analysis for pain-related evoked potentials (PREP) to achieve a higher objectivity and non-biased examination as well as a reduction in the time expended during clinical daily routines. While manually examining, each epoch of an en-semble of stimulus-locked EEG signals, elicited by electrical stimulation of predominantly intra-epidermal small nerve fibers and recorded over the central electrode (Cz), is in-spected for artifacts before calculating the PREP by averag-ing the artifact-free epochs. Afterwards, specific peak-latencies (like the P0-, N1 and P1-latency) are identified as certain extrema in the PREP’s waveform. The proposed automated analysis uses Pearson’s correlation and low-pass differentiation to perform these tasks. To evaluate the auto-mated analysis’ accuracy its results of 232 datasets were compared to the results of the manually performed examina-tion. Results of the automated artifact rejection were compa-rable to the manual examination. Detection of peak-latencies was more heterogeneous, indicating some sensitivity of the detected events upon the criteria used during data examina-tion.