The inverse radiative transfer equation to retrieve atmospheric ozone distribution from the UV-visible satellite spectrometer Global Ozone Monitoring Experiment (GOME) has been modeled by means of a feed forward neural network. This Neural Network Ozone Retrieval System (NNORSY) was trained exclusively on a data set of GOME radiances collocated with ozone measurements from ozonesondes, Halogen Occultation Experiment, Stratospheric Aerosol and Gas Experiment II, and Polar Ozone and Aerosol Measurement III. Network input consists of a combination of spectral, geolocation, and climatological information (time and latitude). In the stratosphere the method globally reduces standard deviation with respect to an ozone climatology by around 40%. Tropospheric ozone can also be retrieved in many cases with corresponding reduction of 10--30%. All GOME data from January 1996 to July 2001 were processed. In a number of case studies involving comparisons with ozonesondes from Hohenpeissenberg, Syowa, and results from the classical Full Retrieval Method, we found good agreement with our results. The neural network was found capable of implicitly correcting for instrument degradation, pixel cloudiness, and scan angle effects. Integrated profiles generally agree to within ¿5% with the monthly Total Ozone Mapping Spectrometer version 7 total ozone field. However, some problems remain at high solar zenith angles and very low ozone values, where local deviations of 10--20% have been observed in some cases. In order to better characterize individual ozone profiles, two local error estimation methods are presented. Vertical resolution of the profiles was assessed empirically and seems to be of the order of 4--6 km. Since neural network retrieval is a mathematically simple, one-step procedure, NNORSY is about 103--105 times faster than classical retrieval techniques based upon optimal estimation.