This study introduces a new approach to identify severe weather events using kernel density estimation. Grids were developed covering the conterminous United States, and the reports for a specific 24-h period on each day from 1960-2008 either were converted to the grid before the method was implemented or were computed as distances from each grid point directly. Several types of map projections, using various grid spacings, were tested. Identification of severe weather clusters required subjective tuning of the bandwidth and the approximate probability density function threshold. Results indicate that kernel density estimation effectively distinguished spatially distinct clusters of severe reports as separate events and was relatively resistant to changes of grid spacing and map projection, as expected. After removal of severe weather clusters with relatively few reports or sparse coverage within the region associated with the cluster, the remaining cases were ranked and classified using a linear-weighted, multivariate index, as in recent studies. The new rankings and classifications of outbreaks were consistent with previous studies.