This article examines the effectiveness of different forms of performance-based adaptive automation (PBAA). Using data from three experiments (N = 10, N = 38, N = 40), different models of algorithm design were compared for their effectiveness in driving PBAA. The following components were varied: type of task (i.e. primary or secondary tasks), baseline of performance data (e.g. moving average), and triggering criterion (i.e. level of deviation from standard performance). The data were generated by operators working with a computer-based simulation of a process control environment. The results showed that none of the models enjoyed a convincing level of effectiveness. The automation algorithms generally achieved higher levels of miss prevention than false alarm prevention. Surprisingly, primary task performance was generally better at driving PBAA than secondary task performance. The results suggest that it may be difficult to design an effective algorithm of PBAA if the work environment is highly complex.