Time series are time-stamped sequences of values which represent a parameter of the observed processes in subsequent time points. Given a set of time series describing a set of similar processes, the model of the behavior of processes is constructed as a range of classiication trees which describe the characteristics of each particular time point in series. An algorithm for matching a sequence of values with the model is used for searching common patterns in the sets of time series, and for predicting the starting time points of undated time series. The algorithm was developed and analyzed in the frame of the study of tree-ring time series. The implementation and the empirical analysis of the algorithm on the tree-ring time series are presented.