With the market becoming increasingly competitive, there is a pressure to deliver systems with more functionality and at the same cost, which thus leads to more complexity in terms of number of components. Moreover, the society is becoming increasingly dependent on these systems for its critical functions. This coupled with shrinking time-to-market and reducing life-cycle, creates a need to and ways to ensure reliability of these complex systems both effciently and quickly. Due to large size and complexity of modern day systems, fault-finding problem is a non-trivial one. Traditionally, Model-Based Diagnosis (MBD) is used to locate faults in the hardware. A prerequisite for MBD is the accurate model of the components. However, modeling of such complex components requires huge effort, time and expertise. Earlier, a spectrum-based hardware solution named BACINOL was proposed to diagnose the hardware system without the aid of a component model. But BACINOL suffers from low diagnosis quality due to large size of ambiguity sets in the final diagnosis. In this thesis, we introduce a new spectrum-based hardware diagnosis technique ANTARES. It attempts to break these ambiguity sets by providing a better estimate of system's False Negative Rate (FNR) information to the diagnosis method. A series of experiments are performed on the ISCAS benchmark circuits to compare the performance of ANTARES with BACINOL and MBD. Results clearly show that ANTARES has better diagnosis quality as compared to BACINOL but has lower performance than MBD.