Mobile applications that provide GPS-based route navigation advice or driver diagnostics are gaining popularity. However, these applications currently do not have knowledge of whether the driver is performing a lane change. Having such information may prove valuable to individual drivers (e.g., to provide more specific navigation instructions) or road authorities (e.g., knowledge of lane change hotspots may inform road design). The present study aimed to assess the accuracy of lane change recognition algorithms that rely solely on mobile GPS sensor input. Three trips on Dutch highways, totaling 158 km of driving, were performed while carrying two smartphones (Huawei P20, Samsung Galaxy S9), a GPS-equipped GoPro Max, and a USB GPS receiver (GlobalSat BU343-s4). The timestamps of all 215 lane changes were manually extracted from the forward-facing GoPro camera footage, and used as ground truth. After connecting the GPS trajectories to the road using Mapbox Map Matching API (2022), lane changes were identified based on the exceedance of a lateral translation threshold in set time windows. Different thresholds and window sizes were tested for their ability to discriminate between a pool of lane change segments and an equally-sized pool of no-lane-change segments. The overall accuracy of the lane-change classification was found to be 90%. The method appears promising for highway engineering and traffic behavior research that use floating car data, but there may be limited applicability to real-time advisory systems due to the occasional occurrence of false positives.