Our goal is to develop a new and reliable statistical method to classify galaxies from large surveys. We probe the reliability of the method by comparing it with a three-dimensional classification cube, using the same set of spectral, photometric and morphological parameters.We applied two different methods of classification to a sample of galaxies extracted from the zCOSMOS redshift survey, in the redshift range 0.5 < z < 1.3. The first method is the combination of three independent classification schemes, while the second method exploits an entirely new approach based on statistical analyses like Principal Component Analysis (PCA) and Unsupervised Fuzzy Partition (UFP) clustering method. The PCA+UFP method has been applied also to a lower redshift sample (z < 0.5), exploiting the same set of data but the spectral ones, replaced by the equivalent width of H$alpha$. The comparison between the two methods shows fairly good agreement on the definition on the two main clusters, the early-type and the late-type galaxies ones. Our PCA-UFP method of classification is robust, flexible and capable of identifying the two main populations of galaxies as well as the intermediate population. The intermediate galaxy population shows many of the properties of the green valley galaxies, and constitutes a more coherent and homogeneous population. The fairly large redshift range of the studied sample allows us to behold the downsizing effect: galaxies with masses of the order of $3cdot 10^{10}$ Msun mainly are found in transition from the late type to the early type group at $z>0.5$, while galaxies with lower masses - of the order of $10^{10}$ Msun - are in transition at later epochs; galaxies with $M <10^{10}$ Msun did not begin their transition yet, while galaxies with very large masses ($M > 5cdot 10^{10}$ Msun) mostly completed their transition before $zsim 1$.
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