Public health surveillance of emerging infectious diseases is an essential instrument in the attempt to control and prevent their spread. This paper presents the R package “surveillance”, which contains functionality to visualise routinely collected surveillance data and provides algorithms for the statistical detection of aberrations in such univariate or multivariate time series. For evaluation purposes, the package includes real-world example data and the possibility to generate surveillance data by simulation. To compare algorithms, benchmark numbers like sensitivity, specificity, and detection delay can be computed for a set of time series. Package motivation, use and potential are illustrated through a mixture of surveillance theory, case study and R code snippets.