The purpose of the present paper is to develop a method, based on equal-time correlation, correlation matrix analysis and surrogate resampling, that is able to quantify and describe properties of synchronization of population neuronal activity recorded simultaneously from multiple sites. Initially, Lorenz-type oscillators were used to model multiple time series with different patterns of synchronization. Eigenvalue and eigenvector decomposition was then applied to identify "clusters" of locally synchronized activity and to calculate a "global synchronization index." This method was then applied to multichannel data recorded from an in vitro model of epileptic seizures. The results demonstrate that this novel method can be successfully used to analyze synchronization between multiple neuronal population series.