A system for detecting and classifying the pain status of a patient comprising: a signal acquisition module comprising a plurality of sensors and/or transducers for measuring and/or obtaining physiological signals and a priori data from a subject; a processing module for processing said physiological signals; and a communicating module for communicating said detected pain status to a receiving unit selected from the group consisting of: a higher processing center, a person, a caregiver a call center, and any combination thereof, wherein the processing module is configured for processing said physiological signals by: a. acquiring a plurality of physiological signals said acquired plurality of physiological signals comprising PPG, GSR and at least one or more physiological signals selected from the group consisting of: skin temperature, ECG, Respiration, EMG, and EEG/FEMG, accelerometer, internal body temperature, EOG, pupil diameter, EGG, LDV, capnograph and blood pressure; b. pre-processing said acquired plurality of physiological signals to form a plurality of pre-processed physiological signals said pre-processing comprising at least one or more chosen from the group consisting of: synchronization, noise filtering, and artifact reduction; and c. processing said plurality of pre-processed physiological signals, the processing comprising: i. extracting a plurality of features from said pre-processed physiological signals; and forming a first vector comprising a set of extracted features; ii. optionally transforming said first vector to a second vector, said transformation comprising normalization, feature selection and hierarchical dimensionality reduction techniques; and iii. detecting and classifying the pain status of a patient by applying a classification function to classify the vector into at least two classes of pain; wherein the classification function is a random forest classifier. a signal acquisition module comprising a plurality of sensors and/or transducers for measuring and/or obtaining physiological signals and a priori data from a subject; a processing module for processing said physiological signals; and a communicating module for communicating said detected pain status to a receiving unit selected from the group consisting of: a higher processing center, a person, a caregiver a call center, and any combination thereof, wherein the processing module is configured for processing said physiological signals by: a. acquiring a plurality of physiological signals said acquired plurality of physiological signals comprising PPG, GSR and at least one or more physiological signals selected from the group consisting of: skin temperature, ECG, Respiration, EMG, and EEG/FEMG, accelerometer, internal body temperature, EOG, pupil diameter, EGG, LDV, capnograph and blood pressure; b. pre-processing said acquired plurality of physiological signals to form a plurality of pre-processed physiological signals said pre-processing comprising at least one or more chosen from the group consisting of: synchronization, noise filtering, and artifact reduction; and c. processing said plurality of pre-processed physiological signals, the processing comprising: i. extracting a plurality of features from said pre-processed physiological signals; and forming a first vector comprising a set of extracted features; ii. optionally transforming said first vector to a second vector, said transformation comprising normalization, feature selection and hierarchical dimensionality reduction techniques; and iii. detecting and classifying the pain status of a patient by applying a classification function to classify the vector into at least two classes of pain; wherein the classification function is a random forest classifier. a. acquiring a plurality of physiological signals said acquired plurality of physiological signals comprising PPG, GSR and at least one or more physiological signals selected from the group consisting of: skin temperature, ECG, Respiration, EMG, and EEG/FEMG, accelerometer, internal body temperature, EOG, pupil diameter, EGG, LDV, capnograph and blood pressure; b. pre-processing said acquired plurality of physiological signals to form a plurality of pre-processed physiological signals said pre-processing comprising at least one or more chosen from the group consisting of: synchronization, noise filtering, and artifact reduction; and c. processing said plurality of pre-processed physiological signals, the processing comprising: i. extracting a plurality of features from said pre-processed physiological signals; and forming a first vector comprising a set of extracted features; ii. optionally transforming said first vector to a second vector, said transformation comprising normalization, feature selection and hierarchical dimensionality reduction techniques; and iii. detecting and classifying the pain status of a patient by applying a classification function to classify the vector into at least two classes of pain; wherein the classification function is a random forest classifier. i. extracting a plurality of features from said pre-processed physiological signals; and forming a first vector comprising a set of extracted features; ii. optionally transforming said first vector to a second vector, said transformation comprising normalization, feature selection and hierarchical dimensionality reduction techniques; and iii. detecting and classifying the pain status of a patient by applying a classification function to classify the vector into at least two classes of pain; wherein the classification function is a random forest classifier.