A hybrid network is proposed for speaker verification (SV). It consists of self organized neural networks and a network of multi layer perceptrons (MLP). The benefits of this SV system are its speed and simplicity. The first experiment used a neural network model (NNM) with frame labelling performed from a client codebook known as NNM-C. Enhanced performance was obtained from this model when compared with the HMM (Hidden Markov Model). The second set of experiments used the NNM with frame labelling from the client and the impostors codebook known as NNM-CI. In the third experiment a new approach used a speaker verification (HMM-MLP) method which combines a HMM based preprocessor with MLP. The output scores of the HMM were used as the inputs to the MLP. Compared with the NNM-C it is more successful since it has the capability of time alignment of the speech signals and the efficient discrimination capability of the neural networks. Moreover the method achieves better performance by the use of more than one feature set for each set of preprocessed parameters. This uses the fact that different feature sets can produce different partitionings of the vector space. The most important contribution of this research was the development and refinement of the NNM SV which incorporate the client barcode into the system design. The idea then is the use of correlation value to measure the degree of similarity or dissimilarity between the client and the impostor as an added information basis to aid the learning process. This measure provides a scalar evaluation of how well the client or the impostor utterance correlates with the client barcode. The integration of the client barcode to the system design has been shown to provide certain advantages to specific client speakers.