Interior noise cancellation for railway train systems is an important means of enhancing passenger comfort and quality of service. This study proposes a novel active noise control (ANC) approach that uses an finite impulse response (IIR) filter and neural network techniques to effectively reduce interior noise. The authors construct a multiple-channel IIR filter module that is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimisation-based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Sufficient stability conditions are derived for the proposed ANC system. Lastly, the authors present the results of a numerical study to test their ANC methodology with realistic interior noise measurement obtained from Korean railway trains.