This dissertation deals with an indoor propagation problem where it is difficult to rigorously obtain the field strength distribution. Access points need to provide good link to the communications backbone of the system. They need to be positioned carefully so that they cover the building with appropriate signal level. Commonly environments can be distinguished as simple or complex ones. The complex environments include non-parallel and non-smooth walls with unknown permittivity. The field strength distribution in the simple environments with parallel walls can be determined by some empirical or deterministic method. Motley-Keenan and ray tracing methods are used to predict field strength at any receiving point of the simple environment. The better results are obtained with ray tracing method when the values of electromagnetic parameters of the walls are obtained by measurements. A free space non-destructive method is introduced for measurement of reflection and transmission coefficients and complex dielectric constant extraction. Application of these methods to the complex environment is very difficult with not accurate results. Absence of real accurate method for the signal strength prediction in indoor environment enables usage of the neural network methods in this area. The neural modeling process includes theoretical and experimental investigations that result in the model based on multilayer perceptron. Inputs are the positions (coordinates) of the access points and of the receiving points, while the output has one neuron to obtain relevant signal strength level. As a training rule we have selected the algorithm that updates the weight and the bias values according to Levenberg-Marquardt optimization model with Bayes regularization. This choice is a result of extensive investigation where neural network architecture and three different learning algorithms have been analyzed. The selected model is tested at particular building environment, such that it's geometrical and construction complexity makes the application of any analytical method to be very difficult. The neural network is trained and tested with measured field strength at various receiving points. The results are very promising. Such trained neural network is used for predicting the field strength distribution as well as for prediction of the optimum access point position. The optimization process for optimal access point position is performed with the PSO algorithm which results are compared with results of Ant Colony Optimization (ACO) and Genetic algorithm. The results show PSO as faster and more accurate algorithm in comaprison with ACO algorithm, but equal accuarte as genetic algorithm.