This paper describes research into the automation of the identification of harlequin and other ladybird species using color images. The automation process involves image processing and the use of an artificial neural network as a classifier. The ultimate aim is to reduce the number of color images to be examined by an expert by pre-sorting the images into correct, questionable and incorrect species. The ladybirds are 3-dimensional and the images have variable resolution. CIELAB has been useful as the color space in this research, as it provides good separation of chroma components from luminance on a color plane. Two major sets of features have been extracted from ladybird images: color and geometrical measurements. The system combination consisted of J48 decision trees which were used to filter out unnecessary features, and multilayer perceptron which was used for classification. Trials using ladybird images showed 92% class match for the species Harmonia axyridis f. spectabilis against Exochomus 4-pustulatus. The identification results are rotation and translation invariant. The methods allow quantitative data on both intra-species and inter-species variation for biodiversity studies.