As computer systems become ever more complex and power hungry, research on dynamic on-the-fly system management and adaptations receives increasing attention. Such research relies on recognizing and responding to patterns or phases in application execution, which has therefore become an important and widely-studied research area. While application phase analysis has received significant attention, much of this attention thus far has focused on simulation-based studies. In these cycle-level simulations without indeterministic operating system intervention, applications display behavior that is repeatable from phase to phase and from run to run. A natural question, therefore, concerns how these phases appear in real system runs, where interrupts and time variability can influence the timing and behavior of the program. Our work examines the phase behavior of applications running on real systems. The key goals of our work are to reliably discern and recover phase behavior in the face of application variability stemming from real system effects and lime sampling. We propose a set of new, "transition-based" phase detection techniques. Our techniques can detect repeatable workload phase information from time-varying, real system measurements with less than 5% false alarm probabilities. In comparison to previous value-based detection methods, our transition-based techniques achieve on average 6X higher recurrent phase detection efficiency under real system variability.