Effective syndromic surveillance depends on effective aberration detection. A variety of aberration detection methods are used in syndromic surveillance systems. However, the relative performance of these algorithms has not been well characterized. We conducted a large simulation study to evaluate the detection properties of six different algorithms: three CUSUM algorithms; two Exponential Weighted Moving Average Models (EWMAs); and a Generalized Linear Model (GLM). Algorithms were tested by taking actual daily syndrome counts from Public Health - Seattle & King County's ongoing syndromic surveillance, spiking these counts with different simulated epidemics, and applying the algorithms to the resulting data. We compared algorithm performance in terms of the sensitivity at each of eight false positive rates. We found that, overall, the GLM model had the greatest sensitivity. However, there was considerable performance heterogeneity across different baseline syndromes, epidemic characteristics, and false positive rates. We created Algorithm Performance Matrices (APMs) to quantitatively compare algorithms across outbreak characteristics.