The Super Dual Auroral Radar Network (SuperDARN) is a network of HF radars that are traditionally used for monitoring phenomena in the Earth's ionosphere at high latitudes. The radar backscatter is due primarily to reflections from plasma irregularities in the ionosphere, known as ionospheric scatter, and to signal reflected from the ground, known as ground scatter. In recent years, SuperDARN has expanded to midlatitudes to provide improved coverage of the auroral region during times of enhanced geomagnetic activity. In addition to high-speed auroral flows, the radars commonly see a variety of low-velocity plasma drift associated with the quiet time midlatitude ionosphere. The traditional method of distinguishing between scatter types in SuperDARN data was developed for high latitudes and depends solely on the Doppler velocity and Doppler spectral width of each data point. This method has proven inadequate for identifying quiet time midlatitude ionospheric scatter. In this paper, we present a new technique for the classification of SuperDARN data, which operates on a distributed range time basis and involves procedures similar to “depth first search.” Using the new method for classification of ground and ionospheric scatter, we show a dramatic improvement in the determination of ionospheric scatter within extended (>1 h) events. Compared to the traditional method, the number of ionospheric measurements resolved increases by more than 50%. The new classification algorithm identifies discrete events of ionospheric scatter and can be applied to statistical analysis of event occurrence and characteristics.