is not shown in detail in fig2 a a three layer neural network in designated by 20 and for example comprises input neurons en 1 to en 7 and further neurons zn 1 to zn 5 and an associated output neuron an 1 using the neural network 20 the spectrum from the first spectrometer 10 is evaluated the backscatter intensities i 1 to i n from preferred wavelengths u03bb i of the schematic diagram in fig2 b are used as inputs for the neural network 20 statements about the quality of the paper to be produced may be obtained from the wavelength intensities i i with i 1 n the statements of the paper quality of output an 1 can be used on the one hand for the feedback control in the stock preparation and on the other hand for feedforward control in the paper machine 1 itself shown in fig3 is another three layer neural network 30 which has input neurons en 8 to en 12 and further neurons zn 6 to zn 9 and an output neuron an 2 using this second neural network 30 data from the spectrometers 10 u2032 and 10 u2033 are evaluated in a similar manner to that in fig2 a it is also advisable to use the moisture 31 of the paper web 110 as a measured variable as a further input variable hence statements about the product quality of the finished paper or board may be obtained on output an 2 furthermore mechanical parameters such as the so called cmt factor the breaking length and the burst pressure can also be obtained it is also possible to provide for the two further spectrometers 10 u2032 and 10 u2033 one dedicated partial neural network each the neural networks according to fig2 and 3.