Switching mode power supply (SMPS) behavior depends entirely upon known component values and often unknown load impedances. Load change produces voltage over- and undershoots and a design-point shift. In most cases, the controller is blind to these shifts. However, load knowledge is an essential design parameter and should precede optimal and adaptive control techniques. System identification algorithms implemented on digital processors open new opportunities in control and system performance. Typical studies in SMPS identification are based on offline steady-state measurements that form nonparametric frequency-domain models. This paper investigates real-time system identification algorithms that generate parametric models - a practical form for pole-placement and root-locus design. Two key metrics are introduced: parameter error and convergence time to describe algorithm accuracy and speed, respectively, after abrupt parameter changes - a common SMPS load scenario. Hardware and simulation results will show that an algorithm called recursive least squares, in its most basic form, could reasonable approximate the input-to-inductor current plant for static loads during startup, but not for abrupt load steps.