Chaff presents multiple issues for aviation, air traffic controllers, and the FAA, including false weather identification and areas where flight paths may need to be altered. Chaff is a radar countermeasure commonly released from aircraft across the United States and is comprised of individual metallic strands designed to reflect certain wavelengths. Chaff returns tend to look similar to weather echoes in the reflectivity factor and radial velocity fields, and can appear as clutter, stratiform precipitation, or deep convection to the radar operator or radar algorithms. When polarimetric fields are taken into account, however, discrimination between weather and non-weather echoes has relatively high potential for success. In this work, the operational Hydrometeor Classification Algorithm (HCA) on the WSR-88D is modified to include a chaff class that can be used as input to a Chaff Detection Algorithm (CDA). This new class is designed using human-truthed chaff datasets for the collection and quantification of variable distributions, and the collected chaff cases are leveraged in the tuning of algorithm weights through the use of a metaheuristic optimization. A final CDA uses various image processing techniques to deliver a filtered output. A discussion regarding WSR-88D observations of chaff on a broad scale is provided, with particular attention given to observations of negative differential reflectivity during different stages of chaff fallout. Numerous cases are presented for analysis and characterization, both as an HCA class and as output from the filtered CDA.