Most applications for location based services rely on a precise determination of
the mobile terminal (MS) location. This paper proposes a method of locating a MS
in a GSM  cellular system based  on the radio  signal strength (RSS)  and use of
neural  network  (NN). The  proposed  algorithm is  best  suited for  urban  and
suburban environments.  Due to  the big  effort during  RSS collection  from the
streets to  realize a  fingerprint database,  predicted RSS-data  of the area of
concern are rather used for this  experiment. An exact modelling of all  effects
in prediction formula in a complex environment is not appropriate in a practical
implementation. Therefore, the predicted data differ from the real one. A system
is developed which calibrates the radio propagation prediction data on the basis
of sample  real collected  measurements. During  this correction  procedure, the
instability  or  stochastic  behavior  of the  RSS  is  taken  into account.  An
algorithm which reduces the  noise in the collected  RSS is applied. Having  the
corrected predicted data, there  is no need of  collecting the RSS from  all the
streets to train the NN. The positioning accuracy is compared for different  pre
-processing methods and calibration shows to reduce the positioning error.