In 1991, US electric utilities spent $1.8 billion on demand-side management (DSM) programs. However, utility DSM efforts vary enormously across the country, concentrated in only a few states. This concentration is partly a function of regulatory reforms that remove disincentives to utility shareholders for investments in DSM programs. A key component of these reforms is recovery of the net lost revenues caused by utility DSM programs. These lost revenues occur between rate cases when a utility encourages its customers to improve energy efficiency and cut demand. The reduction in sales means that the utility has less revenue to cover its fixed costs. This article describes a new method, statistical recoupling (SR), that addresses this net-lost-revenue problem. Like other decoupling approaches, SR breaks the link between revenues and sales. Unlike other approaches, SR minimizes changes from traditional regulation. In particular the revenue risks associated with year-to-year changes in weather and the economy remain with the utility under SR. Statistical recoupling uses statistical models that explain retail electricity sales as functions of the number of utility customers, winter and summer weather the condition of the local economy, electricity price, and perhaps a few other key variables. These models, along with the actual values of the explanatory variables, are used to estimate ＇＇allowed＇＇ electricity sales and revenues in future years. For example, a utility might use quarterly data from 1980 through 1992 to estimate the SR models. The models would then be used to determine allowed revenues for 1993, 1994, and 1995. Five utilities provided data to use in testing this new approach. The empirical results are quite promising. The annual errors are almost all less than 2%. And the 3-year averages for these utilities are less than 1.3%. The lack of patterns across these 3 years and five utilities suggests that statistical recoupling is a robust method.