AbstractDetermining the maturity condition of a large number of individuals is crucial for stock assessment and management of cephalopod populations, but this task is difficult to conduct in practice. We propose a novel approach for maturity stage classification using observer-independent criteria. Relevant morphological variables for classification are determined via decision tree (DT) analysis. Using Illex argentinus and Enteroctopus megalocyathus as case studies, individuals were sexed and assigned to a maturity stage defined by specific macroscopic maturity scales. Also, for each individual, the weight of the gonad, accessory glands/ducts, mantle length, and total weight were recorded and maturity indices were calculated (Hayashi index and gonadosomatic index). Two different DT models were fitted: one considering all maturity stages and the other considering only intermediate maturity stages since these are the most difficult to determine in practice. For the classification of I. argentinus among all stages, the weights of the nidamental gland and oviducts were the most relevant variables for females (misclassification 23%), while spermatophoric complex and testis weights were the key variables for males (misclassification 23%). For classification of intermediate stages only, the nidamental gland and spermatophoric complex weights were the most relevant variables to classify females (misclassification 19%) and males (misclassification 21%), respectively. For E. megalocyathus, the oviducts and ovary weights of females and the terminal organ weight of males were the most relevant variables for classification among all maturity stages (misclassification 16% and 18%, respectively). For intermediate maturity stages, the same variables were most important and misclassification improved to 13% for both sexes. Gonadosomatic and Hayashi's indices were not relevant in either model. DTs based on measurements of cephalopod reproductive systems revealed a simple classification system for maturity stages using only a few variables that are easy to measure in the field and are independent of observer training.