Tornadoes pose a substantial risk to life and property and while advancements in understanding of hazard evolution and forecast communication increases community resiliency, better understanding and quantification of social vulnerability to tornadoes at high spatial resolutions is also needed to increase preparedness and resiliency. To expand this understanding of vulnerability, relationships were examined between census tract-level demographic data, land-use land-cover data, and specific fatality locations for 13 deadly tornadoes where tornado damage path shapefiles were available. These spatially precise datasets were used to examine what demographic variables might be most connected to tornado fatalities and to build a linear predictive statistical model that quantifies relative social vulnerability to tornadoes. The predictive model was then used to create a map of Oklahoma showing relative vulnerability on a census tract-level scale that could be used by decision makers to prepare for, respond to, and recover from tornadoes. Key results of this pilot study include: 1) social vulnerability to tornadoes is higher in urban areas than in rural areas, 2) incorporating as many demographic variables as possible into the predictive statistical model appears to result in more accurate vulnerability maps, 3) vulnerability maps can add useful information to other tools, such as radar-based tornado track estimation products, and 4) the area of developed land within a tornado track is likely related to fatalities.