Method of processing image data in a neural network comprising a plurality of units (14, 16), each unit having a data input, a data output, and at least one processing parameter, wherein processing of input data by means of a respective one of the plurality of units (14, 16) is at least partially determined by the at least one processing parameter of the respective unit, wherein the method implements a multi-channel unit forming a respective one of the plurality of units, and wherein the method comprises: - receiving, at the data input, a plurality of input picture elements representing an image acquired by means of a multi-channel image sensor, wherein the plurality of input picture elements comprise a first and at least a second portion of input picture elements, wherein the first portion of input picture elements represents a first channel of the image sensor and the second portion of input picture elements represents a second channel of the image sensor, - processing of the first and at least second portion of input picture elements separately from each other, and - outputting, at the data output, the processed first and second portions of input picture elements, wherein the multi-channel unit forms a first unit of the neural network, wherein the data outputted through the data output of the multi-channel unit is further processed by at least one subsequent unit (14, 16) of the neural network, wherein the data outputted by a last unit of the neural network comprises a discrete variable classifying the image represented by the plurality of input picture elements received by the first unit, and wherein the neural network implements a classifier for traffic signs; characterized in that: the image sensor is a red-clear-clear-clear sensor, the first and second portions of input picture elements (36, 38) consist of an unequal number of picture elements, respectively, and the processed first and second portions of input picture elements consist of an equal number of picture elements. wherein the method implements a multi-channel unit forming a respective one of the plurality of units, and wherein the method comprises: - receiving, at the data input, a plurality of input picture elements representing an image acquired by means of a multi-channel image sensor, wherein the plurality of input picture elements comprise a first and at least a second portion of input picture elements, wherein the first portion of input picture elements represents a first channel of the image sensor and the second portion of input picture elements represents a second channel of the image sensor, - processing of the first and at least second portion of input picture elements separately from each other, and - outputting, at the data output, the processed first and second portions of input picture elements, - receiving, at the data input, a plurality of input picture elements representing an image acquired by means of a multi-channel image sensor, wherein the plurality of input picture elements comprise a first and at least a second portion of input picture elements, wherein the first portion of input picture elements represents a first channel of the image sensor and the second portion of input picture elements represents a second channel of the image sensor, - processing of the first and at least second portion of input picture elements separately from each other, and - outputting, at the data output, the processed first and second portions of input picture elements, wherein the multi-channel unit forms a first unit of the neural network, wherein the data outputted through the data output of the multi-channel unit is further processed by at least one subsequent unit (14, 16) of the neural network, wherein the data outputted by a last unit of the neural network comprises a discrete variable classifying the image represented by the plurality of input picture elements received by the first unit, and wherein the neural network implements a classifier for traffic signs; characterized in that: the image sensor is a red-clear-clear-clear sensor, the first and second portions of input picture elements (36, 38) consist of an unequal number of picture elements, respectively, and the processed first and second portions of input picture elements consist of an equal number of picture elements. the image sensor is a red-clear-clear-clear sensor, the first and second portions of input picture elements (36, 38) consist of an unequal number of picture elements, respectively, and the processed first and second portions of input picture elements consist of an equal number of picture elements.