Two techniques for exploring relative horizontal accuracy of complex linear spatial features are described and sample source code (pseudo code) is presented for this purpose. The first technique, relative sinuosity, is presented as a measure of the complexity or detail of a polyline network in comparison to a reference network. We term the second technique longitudinal root mean squared error (LRMSE) and present it as a means for quantitatively assessing the horizontal variance between two polyline data sets representing digitized (reference) and derived stream and river networks. Both relative sinuosity and LRMSE are shown to be suitable measures of horizontal stream network accuracy for assessing quality and variation in linear features. Both techniques have been used in two recent investigations involving extraction of hydrographic features from LiDAR elevation data. One confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes yielded better stream network delineations, based on sinuosity and LRMSE, when using LiDAR-derived DEMs. The other demonstrated a new method of delineating stream channels directly from LiDAR point clouds, without the intermediate step of deriving a DEM, showing that the direct delineation from LiDAR point clouds yielded an excellent and much better match, as indicated by the LRMSE.