A computer-implemented image processing method comprising: for a specified multivariate data set, creating a digital image comprising a plurality of superimposed line graphs, each line graph corresponding to a different one of a plurality of variables in the multivariate data set and representing respective values of the variable concerned in successive data items of the multivariate data set; colorizing the created digital image by carrying out a colorization process for each data item of the multivariate data set, the colorization process comprising: for each variable having a positive value in the data item, assigning a color value to pixels representing an area under the line graph for that variable, using a color pre-allocated to that variable and one of the ratio of the value of the variable over a mean value of the data item and the product of the value of the variable with a mean value of the data item; for each variable having a negative value in the data item, assigning a color value to pixels representing an area above the line graph for that variable, using a color pre-allocated to that variable and one of the ratio of the value of the variable over a mean value of the data item and the product of the value of the variable with a mean value of the data item; and summing the color values for pixels where the areas for at least two of the line graphs overlap; and storing the colorized image for use in a data classification process or an anomaly detection process using a deep neural network. for a specified multivariate data set, creating a digital image comprising a plurality of superimposed line graphs, each line graph corresponding to a different one of a plurality of variables in the multivariate data set and representing respective values of the variable concerned in successive data items of the multivariate data set; colorizing the created digital image by carrying out a colorization process for each data item of the multivariate data set, the colorization process comprising: for each variable having a positive value in the data item, assigning a color value to pixels representing an area under the line graph for that variable, using a color pre-allocated to that variable and one of the ratio of the value of the variable over a mean value of the data item and the product of the value of the variable with a mean value of the data item; for each variable having a negative value in the data item, assigning a color value to pixels representing an area above the line graph for that variable, using a color pre-allocated to that variable and one of the ratio of the value of the variable over a mean value of the data item and the product of the value of the variable with a mean value of the data item; and summing the color values for pixels where the areas for at least two of the line graphs overlap; and for each variable having a positive value in the data item, assigning a color value to pixels representing an area under the line graph for that variable, using a color pre-allocated to that variable and one of the ratio of the value of the variable over a mean value of the data item and the product of the value of the variable with a mean value of the data item; for each variable having a negative value in the data item, assigning a color value to pixels representing an area above the line graph for that variable, using a color pre-allocated to that variable and one of the ratio of the value of the variable over a mean value of the data item and the product of the value of the variable with a mean value of the data item; and summing the color values for pixels where the areas for at least two of the line graphs overlap; and storing the colorized image for use in a data classification process or an anomaly detection process using a deep neural network. <EOS>