Monitoring, analyzing and determining the production quality in a complex and long-running process such as in the aluminum production is a challenging task. The domain experts are often overwhelmed by the flood of data being generated and collected and have difficulties to analyze and interpret the results. Likewise, experts find it difficult to identify patterns in their data that may indicate deviations and anomalies that lead to unstable processes and lower product quality. We aim to support domain experts in the production data exploration and identifying meaningful patterns. The existing research covers a broad spectrum of pattern recognition methodologies that can be potentially applied to elicit patterns in data collected from industrial production. Hence, in this paper, we further analyze the applicability of different similarity measures to effectively recognize specific ultrasonic patterns which may indicate critical process deviations in aluminum production.