Abstract. The exact period determination of a multiperiodic variable star based on its luminosity time series data is believed a task requiring skill and experience. Thus the majority of available time series analysis techniques require human intervention to some extent. Relying on the SigSpec technique (Reegen 2007), a fully automated method of period (or frequency) determination from the time series data of variable stars is developed. The SigSpec technique established here employs a statistically unbiased treatment of frequency-domain noise and avoids spurious (i.e. noise induced) and alias peaks to the highest possible extent. We present tests on 386 stars taken from ASAS2 project database. From the output file produced by SigSpec, the frequency with maximum spectral significance is chosen as the genuine frequency. Out of 386 variable stars available in the ASAS2 database, our results contain 243 periods recovered correctly, 88 half periods, 42 different periods etc. Thus SigSpec has the potential to be effectively used for fully automated period detection from variable stars' time series data. The exact detection of periods helps us to identify the type of variability and classify the variable stars, which provides a crucial information on the physical processes effective in stellar atmospheres.