An analysis of hydrograph recessions can be used to identify the parameters of a conceptual catchment storage irnodel and, with the advent of large-scale digital data storage and automated logging systems, it has become desirable to automate recession curve analysis. Various studies have thus reported algorithms used to infer 'baseflow' storage models automatically from recession data. Such algorithms commonly operate by maximising the fit of measured recession data to some <I>a priori</I> function. Here, an alternative approach is taken in which the appropriate <I>form</I> for a catchment saturated zone store is investigated by combining observed recession data to form a Master Recession Curve (MRC). This is done within a software package that offers automated functions to help select recession periods suitable for inclusion within the MRC. These recession periods are combined automatically to form a "prototype" MRC, which can be modified interactively to overcome problems such as unrepresentative or sparse data. The master recession for a catchment is used to calculate an empirical catchment-averaged discharge-relative storage (<I>QΔS</I>) relationship. The method is considered to be general because the <I>QΔS</I> relationship may be of arbitrary form. Examples are given, showing the derivation for three catchments of different <I>QΔS</I> functions.