Understanding perception and the underlying cognitive processes on a behavioral level requires a solution to the feature identification problem: Which are the features on which sensory systems base their computations? What techniques can we use to identify them? Thus one of the central challenges in psychophysics is System Identification: We need to infer the critical features, or cues, human observers make use of when they see or hear. What aspect of the visual or auditory stimulus actually influences behaviour if faced with real-world, complex stimuli? In my laboratory we have developed exploratory, data-driven system identification techniques based on modern machine learning methods to infer the critical features from human behavioural judgments. I will present these methods and show what their benefits are over the traditional “reverse-correlation” approach and the “bubbles technique”.