Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychological neurodevelopmental disorder among children and adolescents, with a prevalence of 5.6% in teenagers aged 12 to 18 years. Its diagnosis is reliable and valid when evaluated with standard criteria for psychiatric disorders, but it is time consuming and requires a high level of expertise to arrive at a correct differential diagnosis. The development of low-cost, fast and efficient tools supporting the ADHD diagnosis process would therefore be important for practitioners, because it should help identify and prevent risks in different populations. In this paper, we study the possibility of detecting ADHD with Natural Language Processing (NLP), based on the analysis of a specific type of adolescent’s autobiographical narratives called Self-Defining Memories (SDMs). (1) We train a Support Vector Machine (SVM) to predict ADHD diagnosis, (2) we attempt to explain its results by exploring lexical information (3) and unfolding the results of the SVM to identify and analyse the linguistic markers associated with each groups. With an accuracy of 92%, the SVM manages to classify texts from both group (ADHD vs Control), revealing a signal specific to autobiographical texts narratives written by people with ADHD. The quality of the detection is confirmed by the interpretative yield of the main markers identified. However, several methodological improvements remain necessary to improve the accuracy and the automation of ADHD diagnosis with stylometric methods.