Detection of hard-alpha inclusions in titanium has been a challenging problem for over two decades. Hard-alpha inclusions are brittle regions of microstructure usually resulting from oxygen or nitrogen contamination. During the high-stressed manufacturing process, these regions initiate cracks which are likely to grow during the service of the component, possibly leading to its failure. It becomes imperative, therefore, to detect these regions early in the manufacturing process. The detection, however, is compounded by the small contrast (consequently weak ultrasonic signal strength) of these inclusions, and the presence of high-level, correlated grain noise with spectral characteristics similar to hard-alpha inclusions. Earlier studies [1] based on model-generated simulation data have suggested that signal matching techniques are promising candidates for the detection of hard-alpha inclusions. One of the primary advantages in the use of these techniques lies in their ability to use flaw signals obtained by ultrasonic modeling as promising filter kernels.