This paper presents a method to use pattern recognition in the analysis of soil water dynamics in time and space simultaneously. Space-time correlation structures are generated by use of a numerical model for the one-dimensional unsaturated flow equation. By applying the method to various soil parameter combinations (unsaturated hydraulic conductivity and dispersion coefficient) and length of infiltration periods it is shown how space-time dependence of soil water changes. The method clearly visualizes effects of changes in infiltrated input and soil heterogeneities which are not noticeable in the original soil water time series. Application of the method to field data reveals small-scale heterogeneities and vertical and horizontal variability in hydraulic properties. The method possesses special advantages when analyzing time- and space-dependent properties simultaneously. Since the method gives a statistical measure of the dependent property that varies within the space-time field, it can be used to interpolate the fields to points where observations are not available, to estimate spatial or temporal averages from discrete observations, and to define regions (spatial or temporal) where observations are the most efficient.