The assumption of a particular type of distribution of rainfall cells in space is needed for the formulation of several space-time rainfall models. In this study, weather radar-derived rain rate maps are employed to evaluate different types of spatial organization of rainfall cells in storms through the use of distance functions and second-moment measures. In particular the spatial point patterns of the local maxima of rainfall intensity are compared to a completely spatially random (CSR) point process by applying an objective distance measure. For all the analyzed radar maps the CSR assumption is rejected, indicating that at the resolution of the observation considered, rainfall cells are clustered. Therefore a theoretical framework for evaluating and fitting alternative models to the CSR is needed. This paper shows how the ''reduced second-moment measure'' of the point pattern can be employed to estimate the parameters of a Neyman-Scott model and to evaluate the degree of adequacy to the experimental data. Some limitations of this theoretical framework, and also its effectiveness, in comparison to the use of scaling functions, are discussed. ¿ American Geophysical Union 1996