Residential wood combustion represents 0.5% of the United States' primary energy consumption yet is responsible for over one-third of the primary PM2.5 emissions nationwide. Cordwood stoves are a class of residential space heating devices with significant prospects for emissions reduction. Emissions from woodstoves are a result of incomplete combustion due to poor mixing, low chamber temperatures, low residence times of the fuel/air mixture, and/or an overall lack of available oxygen. These challenges are further exacerbated by user-error, producing more real-world emissions than what certification testing may suggest, as most devices on the market rely on manual controls for air supply and refueling. To combat this, we have envisioned an intelligent stove that utilizes a minimal set of measurement sensors and a heuristic control strategy to actively modulate incoming air to enhance stove combustion performance, thereby eliminating user-error as a factor for emissions production. Critical performance metrics such as the heat release rate, instantaneous stove efficiency, combustion stoichiometry and wood moisture content can all be estimated using combinations of the stove temperature, weight and airflow rates. These parameters are then used in a feedback control algorithm to optimize the variable application of combustion air as well as reduce the burden on the operator by providing recommendations for refueling and replacement of components through automated, intelligent decision-making. Results from preliminary experiments have exhibited trends between the airflow velocity and stove temperatures, as well as demonstrated the reliability of low-cost sensors such as strain gauges and K-type thermocouples in producing repeatable measurements.