Spiking Neural Networks (SNNs) model the dynamics and learning capabilities of the brain in a more biologically inspired way than previous generations of neural networks. However, training these networks is still problematic due to over-training and weight instabilities. This research focuses on the design and implementation of a more biologically inspired training algorithm, based on the Spike Timing Dependent Plasticity (STDP) learning rule where weight changes are dependant on the temporal distribution of the input data; the algorithm cross correlates (CC) similarities in the input data across all classes and hence implements global training. The implementation required that the original STDP training rule be extended to take into account both global and local similarities in the input data across all classes. Other novel features of the approach are the use of multiple synaptic connections, axonal delays and dynamic thresholds. Results from the benchmark problems, Iris and Wisconsin Breast Cancer data, for two and three layer SNNs are presented. The three layer SNN has showed a classification accuracy which was superior to the two layer network and comparable to other approaches. Unlike the two layer SNN, the three layer structure utilised the CC rule together with dynamic thresholds where the latter correlated similarities in the spatial patterns across the different data classes. Further investigations were carried out on a temporal application, speech corpus TI46 data. The TI46 data was pre-processed using the most popularly applied method: Mel-Frequency Cepstral Coefficients (MFCC). The results obtained are highly comparable with results from more complex state-of-the-art classification systems such as Liquid State Machines. The proposed learning algorithm facilitates competition between neurons eradicating the problem of a bi-polar weight distribution, consequently stabilising learning. Results have demonstrated that this novel learning technique produces stability in the learning process and is a significant contribution in enabling SNNs to be applied to realistic real world classification problems.