Interactions between neurones can be analysed by simultaneously recording from several cells and computing correlation functions between the respective activities. Recent studies have revealed that neuronal responses are often synchronous and exhibit an oscillatory temporal structure. These two properties are commonly assessed together from correlation functions. In order to evaluate these variables independently a method was devised for the quantification of a generalized Gabor function that was fitted to the correlograms. The performance of the method was tested on a large data set from cat area 17 and its stability was examined with respect to its dependence on the number of free parameters. The results demonstrate that the proposed fitting algorithm is robust in that it is rather independent of starting conditions and converges to optimal fits even with different settings of free variables. The fitted correlation function allow for an automatic and independent classification of synchrony on the one hand and oscillatory firing patterns on the other.