Ultra-wideband ground penetrating radar (GPR) systems are useful for extracting and displaying information for target recognition purposes. The frequency content of projected signals is designed to match the size and type of prospective targets and environments. Target signatures whether in the time, frequency, or joint time-frequency domains, will substantially depend on the target's burial conditions such as the type of soil, burial depth, and the soil’s moisture content. Such returned echoes from two targets for several moisture contents and burial depths in a soil with known electrical properties were simulated earlier by using a Method-of-Moments (MoM) code. The signature template of each target was computed using a time-frequency distribution of the returned echo when the target was buried at standard conditions, namely, a selected depth in the soil with a selected moisture content. For any returned echo the relative difference between the likewise computed target signature and a template signature was computed. That signature difference, chosen as objective function, or cost function, could then be minimized by adjusting the depth and moisture content, now taken to be unknown parameters. This can be done using the differential evolution method (DEM) together with our target translation method (TTM). The template that gave the smallest value of the minimized objective function for the returned echo signified the classification, and the corresponding values of the depth and moisture parameters turned out to be good predictions of the actual target depth and soil moisture content. Here, we implement a more efficient and faster running version of this classification method on a stepped-frequency continuous-wave (SFCW) GPR system. We demonstrate the ability to classify mines or mine-like targets buried underground from measured GPR signals. The targets are buried either in an indoor sandbox or in a test field at the Swedish Explosive Ordnance Disposal and Demining Center (SWEDEC) at Eksjo, Sweden.