The detection of cortical auditory evoked potentials (CAEP), which are part of the electroencephalogram (EEG) in reaction to acoustic stimuli, has important applications such as determining objective audiograms. The detection is usually performed by a human operator, with support from often basic signal processing methods. This paper presents a novel mechanism for the detection of CAEPs, which is fully automatic and stops the measurement when a given confidence is reached. This proposed detector comprises of three stages. First, a feature extraction by a wavelet transform parameterizes the time domain EEG signal by only few transform coefficients. This feature vector is then classified by a neural network which yields a binary vote on every EEG segment. Finally, a sequential statistical test is performed on successive classifications; this stops the measurement if a specified decision confidence has been reached. The adjustment of the detector according to a clinical database is discussed. Thus adjusted, the proposed CAEP detection scheme is applied to a study, and compared with a human operator. The results demonstrate that this method can attain similar results, but outperforms the human expert for stimulation levels close to the hearing threshold.