Signal Alignment Theory outlines a universal pattern framework rooted in wave dynamics, identifying a core set of seven conserved patterns and five additional semi-conserved patterns. The conserved patterns are present in all coherent systems, while the semi-conserved patterns occur selectively, often signaling adaptability or transition. These patterns emerge across all systems of scale, from atomic to societal, due to the recursive nature of waves. That is, their tendency to produce patterns that reinforce or transform across scales. Central to this framework is the role of signal: how clearly structured information emerges from noise, how it aligns, and how its signal-to-noise ratio (SNR) governs system stability. This paper introduces the theory and provides a diagnostic structure for identifying, mapping, and analyzing perturbation patterns in complex systems. This approach offers a foundational lens for recognizing emergent instability before it cascades, and for recalibrating systems toward sustained coherence.