Disconnections between structures in the brain have long been hypothesized to be the mechanism behind numerous disease states and pathological behavioral phenotypes. Advances in diffusion weighted imaging (DWI) provide an opportunity to study white matter, and therefore brain connectivity, in great detail. DWI-based research assesses white matter at two different scales: voxelwise indexes of anisotropy such as fractional anisotropy (FA) are used to compare small units of tissue and network-based methods compare tractography-based models of whole-brain connectivity. We propose a method called local termination pattern analysis (LTPA) that considers information about both local and global brain connectivity simultaneously. LTPA itemizes the subset of streamlines that pass through a small set of white matter voxels. The "local termination pattern" is a vector defined by counts of these streamlines terminating in pairs of cortical regions. To assess the reliability of our method we applied LTPA exhaustively over white matter voxels to produce complete maps of local termination pattern similarity, based on diffusion spectrum imaging (DSI) data from 11 individuals in triplicate. Here we show that local termination patterns from an individual are highly reproducible across the entire brain. We discuss how LTPA can be deployed into a clinical database and used to characterize white matter morphology differences due to disease, developmental or genetic factors.