Reservoir computing is a framework within which an arbitrary non-trainable reservoir, e.g. a network with a fixed structure, is used for computing, and the output of the reservoir is supplied with a simple classification or regression algorithm, which is trained to yield required results. In the current study, we investigate the possibility of a simple spiking neural network (SNN) based on the extended Brusselator model to serve as the reservoir for binary pattern recognition. We train various classifiers on data obtained from the four-neuron SNN and show that when there are two and three digits in a binary pattern three and six unique patterns can be distinguished, respectively, with the positive predictive value not less than 89 %. The obtained results can be used for developing a fast-training chemical computer taking advantage of its hardware structure.