Publisher Summary This chapter analyzes complex sensory data by nonlinear artificial neural networks. The problems associated with prediction in sensory science can be seen as twofold. The first is that data are often nonlinear. Simple transformations such as logarithms may help, but it may be that the nonlinearities are more complex. A second problem is that while good models may be obtained where the variation between samples is large, this is seldom the case where variation between samples is very small. Nonlinear methods like neural networks should be taken into account to detect the nonlinear relations in the data. Nonlinear methods like neural networks should be taken into account to detect the nonlinear relations in the data. The transfer function used in the neural net is designed to detect both linear and nonlinear relations in the data. For analyzing complex sensory data process it is required to use different tools giving corresponding results but with different degrees of accuracy. It is very important for the user of neural nets and principal component regression/partial least squares regression (PCR/PLS) to understand the limitations and pitfalls.