Time series clustering has become an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics. Unfortunately, existing methods for time series clustering that rely on the actual time series point values can become impractical since the methods do not scale well for longer time series, and many clustering algorithms do not easily handle high dimensional data. In this paper we propose a scalable method for time series clustering that replaces the time series point values with some global measures of the characteristics of the time series. These global measures are then clustered using a selforganising map, which performs additional dimension reduction. The proposed approach has been tested using some benchmark time series previously reported for time series clustering, and is shown to yield useful and robust clustering. The resulting clusters are similar to those produced by other methods, with some interesting variations that can be intuitively explained with knowledge of the global characteristics of the time series.