Hyperspectral imaging sensors commonly employ multiple apertures or focal planes for broad spectral coverage. These designs often result in spatial misregistration artifacts between the spectral regions. Unknown misregistration errors of fractions of a pixel cannot be corrected and can have a negative impact on target detection performance, especially for targets that are subpixel. The work here analyzes the impact of band-to-band misregistration on hyperspectral target detection performance. Synthetic imagery was used to simulate various amounts of sub-pixel misregistration between the visible (VIS) and near infrared (NIR) regions of the optical spectrum. Scenes were created with vehicles placed as targets. Target detection algorithms were applied using both the full spectrum misregistered imagery, and the VIS and NIR bands separately. Receiver operating characteristic curves were used to assess the performance of each algorithm for each target. Results indicate that statistical target detection algorithms are less sensitive to band-to-band misregistration than geometric algorithms. Results also indicate that in some cases it is more beneficial to use full spectrum misregistered imagery rather than applying target detection algorithms to the VIS and NIR bands separately, even for large amounts of sub-pixel misregistration.