The WIde-field Nearby Galaxy-cluster Survey (WINGS) is a project aiming at the study 
of the galaxy populations in clusters in the local universe ( 0.04<z<0.07) and the 
influence of environment on the physical properties of galaxies. 
This survey provides a high quality set of spectroscopic data for ~6000 galaxies 
in 48 clusters. The study of such a large amount of objects requires automatic 
tools capable of extracting as much information as possible from the data.
I will describe a stellar population synthesis technique that reproduces the observed 
optical spectra of galaxies performing an analysis based on spectral fitting of 
the stellar content, extinction and, when possible, metallicity.
A salient feature of this model is the possibility of treating dust extinction 
as a function of age, allowing younger stars to be more obscured than older ones. 
As far as I know, this feature is taken into account for the first time in a spectral 
fitting technique.
A set of template spectra spanning a wide range of star formation histories is built, 
with features closely resembling those of typical spectra in the WINGS sample in terms 
of spectral resolution, noise and wavelength coverage. This method of analyzing these 
spectra allows to test the reliability and the uncertainties related to each physical 
parameter that are inferred. The well-known degeneracy problem, i.e. the non-uniqueness 
of the best fit solution (mass and extinction in different age bins), can be addressed 
by assigning adequate error bars to the recovered parameters. The values found in this way, 
together with their error bars, identify the region of parameter space which contains 
all the possible solutions for a given spectrum. A comparison test was also performed on 
a WINGS subsample, containing objects in common with the Sloan Digital Sky Survey, 
yielding excellent agreement.
Stellar masses and star formation histories are the main outcomes of this analysis.
This technique is also applied to composite spectra of cluster galaxies, exploiting 
the higher signal-to-noise ratios, with respect to single spectra.