Ten years of Oregon Seed Certification Service (OSCS) preharvest field inspections converted from a nonspatial database to a geographic information system (GIS) were analyzed for patterns in spatial distribution of occurrence and severity of the 36 most common weeds of grass seed crops. This was done under the assumptions that those patterns would be primarily consequences of interactions among farming practices, soil properties, and biological traits of the weeds, and that improved understanding of the interactions would benefit the grass seed industry. Kriging, Ripley's K-function, and both Moran's I spatial autocorrelation and Getis-Ord General G high/low clustering using the multiple fixed distance band option all produced roughly similar classifications of weeds possessing strongest and weakest spatial clustering patterns. When Moran's I and General G analyses of maximum weed severity observed within individual fields over the life of stands were conducted using the inverse distance weighting option, however, results were highly sensitive to the presence of a small number of overlapping fields in the 10-yr record. Addition of any offset in the range from 6 to 6,437 m to measured distances between field centroids in inverse distance weighting matrices removed this sensitivity, and produced results closely matching those for the multiple fixed distance band method. Clustering was significant for maximum severity within fields over the 10-yr period for all 43 weeds and in 78% of single-year analyses. The remaining 22% of single-year cases showed random rather than dispersed distribution patterns. In decreasing order, weeds with strongest inverse-distance spatial autocorrelation were German velvetgrass, field bindweed, roughstalk bluegrass, annual bluegrass, orchardgrass, common velvetgrass, Italian ryegrass, Agrostis spp., and perennial ryegrass. Of these nine weeds, distance for peak spatial autocorrelation ranged from 2 km for Agrostis spp. to 34 km for common velvetgrass. Weeds with stronger spatial autocorrelation had greater range between distance of peak spatial autocorrelation and maximum range of significance. Z-scores for General G high/low clustering were substantially lower than corresponding values for Moran's I spatial autocorrelation, although the same two weeds (German velvetgrass and field bindweed) showed strongest clustering using both measures. Simultaneous patterns in Moran's I and General G implied that management practices relatively ineffective in controlling weeds usually played a greater role in causing weeds to cluster than highly effective practices, although both types of practices impacted Italian ryegrass distribution. Distance of peak high/low clustering among perennial weeds was smallest (1 to 3 km) for Canada thistle, field bindweed, Agrostis spp., and western wildcucumber, likely indicating that these weeds occurred in patchy infestations extending across neighboring fields. Although both wild carrot and field bindweed doubled in average severity over the period from 1994 to 2003, wild carrot was the only weed clearly undergoing an increase in spatial autocorrelation. Soil chemical and physical properties and dummy variables for soil type and crop explained small but significant portions of total variance in redundancy and canonical correspondence analysis of weed occurrence and severity. Fitch-Morgoliash tree diagrams and Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) ordinations revealed substantial differences among soil types in weed occurrence and severity. Gi∗ local hot-spot clustering combined with feature class to raster conversion protected grower expectations of confidentiality while describing dominant spatial features of weed distribution patterns in maps released to the public.