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The multimodal biometric system based on palm print, finger geometry and dorsal palm vein modalities is proposed, specifically for high-security applications. This paper aims to prove that the proposed multimodal biometric system is an adaptive, error-resilient and robust system. A novel'Optimum Weights Algorithm'makes the system adaptive and provides the best possible accuracy. An erroneous database of 100 users is collected to check an error resilience and robustness of the multimodal system. Palm print, finger geometry and dorsal palm vein feature extraction algorithms are used to extract feature vectors for all three modalities. Accuracy prediction is made by plotting the ROC curve for the multimodal system and estimating GARmin from that ROC curve. It is observed that the accuracies of the finger geometry and dorsal palm vein modalities remain unaffected for the erroneous database; however, there is a …