This paper describes a four-step method to analyze the utility bills and weather data from multiple buildings to target and measure the effectiveness of energy efficiency opportunities. The first step is to create a three-parameter change-point regression model of energy use versus weather for each building and type of energy. The three model parameters represent weather independent energy use, the building heating or cooling slopes and the building balance-point temperature. The second step is to drive the models with typical meteorological year (TMY2) weather data to determine Normalized Annual Consumption (NAC) for each type of energy. The third step is to create a sliding NAC with each set of 12 sequential months of utility data. The final step is to benchmark the NACs and coefficients of multiple buildings to identify average, best and worst energy performers, and how the performance of each building has changed over time. The method weather normalizes energy use, prioritizes buildings for specific energy-efficiency retrofits, and tracks weather-normalized changes in energy use. This paper describes the method, and then demonstrates the method through a case study analysis of energy use data from 14 Midwestern hospitals.