Background: Antimicrobial-resistant infections result in hospital stays costing between $18,000 and $29,000. As of 2009, Centers for Medicare and Medicaid Services no longer upgrade payments for hospital-acquired infections. Hospital epidemiologists monitor and document rates of individual resistant microbes in antibiogram reports. Overall summary measures capturing resistance within a hospital may be useful. Objectives: We applied four techniques (L1- and L2-principal component analysis (PCA), desirability functions, and simple summary) to create summary measures of resistance and described the four summary measures with respect to reliability, proportion of variance explained, and clinical utility. Methods: We requested antibiograms from hospitals participating in the University HealthSystem Consortium for the years 2002–2008 (n=40). A clinical team selected organism-drug resistant pairs (as resistant isolates per 1,000 patient days) based on 1) virulence, 2) complicated or toxic therapies, 3) transmissibility, and 4) high incidence with increasing levels of resistance. Four methods were used to create summary scores: 1) L1- and L2-PCA: derived multipliers so that the variance explained is maximized; 2) desirability function: transformed resistance data to be between 0 and 1; 3) simple sum: each resistance rate was added and divided by the square root of the total number of microbes summed. Simple correlation analyses between time and each summary score evaluated reliability. For each year, we calculated the proportion of explained variance by dividing each summary score’s variance by the variance in the original data. Clinical utility was checked by comparing the trends for all of the individual microbe’s resistance rates to the trends seen in the summary scores for each hospital. Results: Proportion of variance explained by L1- and L2-PCA and the simple sum was 0.61, 0.62, and 0.29 respectively. Simple sum and L1- and L2-PCA summary scores best followed the trends seen in the individual antimicrobial resistance rates; trends in desirability function scores deviated from those seen in individual trends of antimicrobial resistance. L1- and L2-PCA summary scores were more influenced by MRSA rates, and the simple sum score was less influenced. Pearson correlation coefficients revealed good reliability through time. Conclusion: Deriving summary measures of antimicrobial resistance can be reliable over time and explain a high proportion of variance. Infection control practitioners and hospital epidemiologists may find the inclusion of a summary score of antimicrobial resistance beneficial in describing the trends of overall resistance in their yearly antibiogram reports.