The paper presents a method for blind separation of auditory event-related brain responses (ERPs) into independent components using independent component analysis (ICA). ICA decomposes ERPs recorded from multiple scalp sensors into a sum of components with fixed scalp distributions and maximally independent time courses. Unlike dipole-fitting methods, ICA does not model the locations of generators in the head and minimizes higher-order dependencies. Applied to detected and undetected target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each major response peak into one or more ICA components with simple scalp distributions. Three components were active only when the subject detected the targets, three when the target was undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. The major features of the decomposition were robust across sessions and changes in sensor number and placement. This method can be used to compare responses from multiple stimuli, task conditions, and subject states. The ICA algorithm is computationally efficient and can handle data from a large number of channels. The method is particularly effective at detecting common response topography in multiple response conditions and quantifying differences between conditions in activation strength of multiple components.The paper presents a method for blind separation of auditory event-related brain responses (ERPs) into independent components using independent component analysis (ICA). ICA decomposes ERPs recorded from multiple scalp sensors into a sum of components with fixed scalp distributions and maximally independent time courses. Unlike dipole-fitting methods, ICA does not model the locations of generators in the head and minimizes higher-order dependencies. Applied to detected and undetected target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each major response peak into one or more ICA components with simple scalp distributions. Three components were active only when the subject detected the targets, three when the target was undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. The major features of the decomposition were robust across sessions and changes in sensor number and placement. This method can be used to compare responses from multiple stimuli, task conditions, and subject states. The ICA algorithm is computationally efficient and can handle data from a large number of channels. The method is particularly effective at detecting common response topography in multiple response conditions and quantifying differences between conditions in activation strength of multiple components.