Spikes are an important part of information transmission between neurons in the biological brain. Biological evidence shows that information is carried in the timing of individual action potentials, rather than only the firing rate. Spiking neural networks are devised to capture more biological characteristics of the brain to construct more powerful intelligent systems. In this paper, we extend our newly proposed supervised learning algorithm called DL-ReSuMe (Delay Learning Remote Supervised Method) to train multiple neurons to classify spatiotemporal spiking patterns. In this method, a number of neurons instead of a single neuron is trained to perform the classification task. The simulation results show that a population of neurons has significantly higher processing ability compared to a single neuron. It is also shown that the performance of Multi-DL-ReSuMe (Multiple DL-ReSuMe) is increased when the number of desired spikes is increased in the desired spike trains to an appropriate number.