RESUMO
Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre-ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef-forts on identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179-193).