In intensive care, clinical information systems permanently record more than one hundred time dependent variables. Besides the aim of recognising patterns like outliers, level changes and trends in such high-dimensional time series, it is important to reduce their dimension and to understand the possibly time-varying dependencies between the variables. We discuss statistical procedures which are able to detect patterns of dependencies within multivariate time series.