This work is aimed at developing a multi-modal, multi-sensor based Person Authentication System (PAS) using JDL model. This research investigates the necessity of multiple sensors, multiple recognition algorithms and multiple levels of fusion and their efficiency for a Person Authentication System (PAS) with face, fingerprint and iris biometrics. Multiple modalities address the issues of non-universality encountered by unimodal systems. The PAS can be aptly addressed as 'smart' since several environmental factors have been considered in the design. If one sensor is not functional, others contribute to the system making it fault-tolerant. Smartness has been very tactfully administered to the processing module by employing different efficient algorithms for a given modality. Selection of the recognition algorithms is rooted on the attributes of the input. Multiplicity has been employed to establish a unanimous decision. Information fusion at various levels has been introduced. Sensor level fusion, local decision level fusion at algorithmic level and global decision level fusion provide the right inference. A multitude of decisions are fused locally to decide the weightage for the particular modality. Algorithms are tagged with weights based on their recognition accuracy. Weights are assigned to sensors based on their identification accuracy. Adaptability is incorporated by modifying the weights based on the environmental conditions. All local decisions are then combined to result in a global decision about the person. The final aggregation concludes whether 'The Person is Authenticated or not'.