Humans are integrated closer to computers every day, and computers are taking over many services that used to be based on face-to-face contact between humans. This has prompted an active development in the field of biometric systems. A biometric can be described as a measurable physical and/or behavioral trait that can be captured and used to verify the identity of a person. The use of biometric information has been known widely for both person identification and security applications. This work is concerned with the use of speech and fingerprint features for controlling access to the facility that need to be protected from the intrusion of unauthorized persons. For speaker recognition, the linear predictive analysis based features were used. A vocabulary of 46 speech samples is built for 10 speakers, where each authorized person is asked to utter every sample 10 times. Two different modes are considered in identifying individuals according to their speech samples. In the closed-set speaker identification, the system gives identification rates in the range of 84% to 97%. Applying the preprocessing steps to improve the representation of speech features, up to 100% identification rate was obtained using Linear Predictive Cepstral Coefficients (LPCC). The open-set speaker verification mode is presented for 213 trials of randomly text-prompt, where the best obtained verification rate is about 99% using city-block distance. Statistical moments and spectral features that are extracted from Wavelet transform for closed and open-set fingerprint recognition are investigated to verify persons' identity. Fingerprints of 49 persons (32-authorized and 17-unauthorized) were taken as testing data. Each authorized person is asked to give 10-instances of his right forefinger print. The results have shown that the wavelet descriptors are efficient representation that can provide reliable recognition for large input variability, while the features derived from statistical moments have shown poor recognition rates and inconsistent behavior. In the closed-set fingerprint recognition, the obtained recognition rates are below 90% due to the imperfections in the fingerprint images that negatively affect the recognition rate. After applying the preprocessing steps, the results are improved and about 100% recognition rates are obtained in some favorable conditions concerning the selection of the wavelet decomposition level and the use of the proposed Wavelet-Bands Selection Features. The open-set fingerprint verification mode is also presented for 290 trials from 29 persons, where the obtained verification rates are in the range of 94% to 97% for Euclidean and city-block distance, respectively. The combination of both biometric information (the speech and fingerprint) achieved more reliable decision rates than speaker or fingerprint verification subsystem. The performance of the entire system is enhanced, where the obtained verification rates are greater than 97%.