A potential method is presented for combining data collected as part of the CCAMLR ecosystem monitoring program (CEMP) into a single index for each of predator, prey and environmental parameters. The paper is divided into four main parts. The first part develops the proposed method of forming summary indices, which is based on the usual theory of multivariate statistics and takes into account the covariance between parameters. The second part reports on a Monte Carlo simulation study that examines the robustness of the indices to missing data and the degree of correlation between parameters. These trials show that missing values were unlikely to be a problem for time series of parameters that are highly correlated (>0.6). Criteria for inclusion of parameters in the indices are discussed when parameters are moderately or poorly correlated. The third part uses further simulation tests to examine the power of the statistical procedure adopted by WG-EMM in 1996 for identifying anomalies in CEMP parameters. The power of the procedure to detect anomalies was found to fall to low levels once more than a few anomalous values have appeared in the data. An alternative procedure, using estimates of the mean and variance of baseline time series, was found to have consistently better statistical power regardless of the accumulation of anomalies. The last section outlines an approach to the further development of CEMP indices for application in CCAMLR.