This paper presents a "neural cancellation filter" capable of segregating weak targets from competing harmonic backgrounds, and a model of concurrent vowel segregation based on this filter. The elementary cancellation filter comprises a delay line and an inhibitory synapse. Every peripheral channel is processed by a similar filter tuned to the period of the competing sound, to suppress its correlates within the neural discharge pattern. Combined with a pattern matching model based on autocorrelation functions summed over all channels, the filter is used to form a model of concurrent vowel identification. The model predicts both the number of vowels reported for each stimulus (when subjects are allowed to report one or two), and the identification rate. It belongs to the class of "harmonic cancellation" models that are supported by experimental evidence that vowels mixed with competing sounds are better identified when the competing sounds are harmonic. It successfully explains the improvement of identification with DF0 observed in conditions where the target vowel level was low (-20 dB) relative to the competing vowel. Two alternative schemes using the same filter are also considered. One derives a "place" representation from the magnitude of the filter output. The other uses the ratio of filter input/output to select channels.