Deep bed filtration is commonly applied in clarification of dilute suspensions of particles ranging in size from about 0.1 to 50 mu m. A suspension carrying solid particles of different sizes is passed through the porous bed of defined geometrical characteristics. It has been found that sizes of suspended particles and their distribution are very important physical parameters that influence deep bed filter efficiency. During the filtration process the bed porosity decreases, whereas interficial velocity increases due to the particle accumulation in filter bed. Mathematical model has been developed under the assumption that the plug flow model approximates flow of suspension through the bed. The second assumption is that a deposition kinetics is a function of local suspension’s particle distribution and locally deposited particle distribution. In order to obtain the experimental data needed for determination of the process kinetics, a set of experiments has been carried out. So obtained experimental data consisted of the local suspension and deposit particle distribution values and also of the local rate values. All three empirical distributions are approximated by standard Logarithm-Normal distribution function. Each distribution is defined by two LN function parameters. Rate distribution parameters are formally dependent on the parameters that define suspension and deposit distribution. This relation has been established using the general regression neural network (GRNN). Thus defined model enables solving the system within given boundary conditions by approximating distribution functions with sums and using orthogonal collocation method for transformation of partial differential equations into a system of ordinary differential equations. Developed method can be applied in process simulation as long as the input concentration and distribution are within the range of experimental values for the kinetics determination. To test the developed method, experiments were conducted on the pilot scale deep bed filter having total height of Im and diameter of 0.1m. The results show that a very complex process, as is deep bed filtration, can be successfully described using hybrid neural network.