The Multiphase biometric System combines multiple biometric modalities or phases to enhance identification or authentication accuracy, It includes physical characteristics of an individual such as fingerprint and patterns of behavior, such as signature. Feature level fusion with normalization techniques aims to create a more robust, discriminative, and consistent multiphase biometric system by standardizing and integrating information from diverse sources effectively. Both LPQ (Local Phase Quantization) and LBP (Local Binary Patterns) are most effective techniques for feature extraction from fingerprint images. The important features extracted for signature analysis are pen pressure, azimuth, and elevation. Multiphase biometric systems leverage the strengths of multiple biometric modalities to enhance recognition accuracy, performance, security, and user acceptance compared to systems based on a single modality. Prior to the fusion of feature sets from two biometric modalities, we normalize the scores using z-score normalization method as the two feature sets resulted with heterogeneous data. Finally, the proposed work on multiphase biometric by the fusion of fingerprint and signature provide the good results.