Affective Computing is a multidisciplinary field with high potential in many human computer interaction applications including the medical field. One growing application is the emotion and stress recognition for early intervention of depression, stress management, risk prevention as well as monitoring individuals’ mental health. In this context, various modalities ranging from facial, speech, text and biosignal analysis have been adopted for the purpose of emotion and stress recognition. Among these modalities, psychophysiological signals have the valuable advantage as “honest signals”: they cannot be easily triggered by any conscious or intentional control and are continuously available. Biosignals acquired through wearable sensors, add the convenience of mobile implementation in real-life in-the-wild applications. This paper presents an automated processing workflow for the psychophysiological recognition of emotion and stress states. Our proposed workflow allows processing biosignals in their raw state as obtained from wearable sensors. It consists of five stages, for which various Matlab-based methods have been implemented allowing 1) Biosignal Preprocessing: raw data conversion, relevant information selection, artifact and noise filtering, sliding window decomposition, 2) Feature Extraction: from different mathematical groups including amplitude, frequency, linearity, stationarity, entropy and variability, 3) Feature Selection: dimension reduction and computation enhancement using forward selection, backward elimination and brute force feature methods, 4) Classification: machine learning using Support Vector Machine, k-Nearest Neighbor and Random Forest algorithms, 5) Evaluation: performance matrix computation based on k-cross, leave-one-subject-out cross and split validations. All workflow stages are integrated into embedded functions allowing an automated execution of the recognition process. The next steps include further development of the algorithms and the integration of the various tools into an easy-to-use system with graphical interface, satisfying the needs of medical and psychological staff. Our automated workflow was evaluated using our Multimodal Affective Corpus (the uulmMAC Database) previously published for Affective Computing in Human-Computer Interaction.