Water distribution systems (WDSs) are critical infrastructure for modern cities and towns. These systems experience on-going deterioration since installation, but the detection of anomalies is difficult since WDSs are typically large in scale and most parts are buried underground. Despite that many techniques are available in the market for anomaly detection in water networks (such as acoustic correlation for leak detection and ultrasonic sounding for wall thickness measurement), they are not cost-effective for large-scale applications and the types of anomalies can be detected are limited. A promising alternative is hydraulic transient-based pipeline condition assessment and feature diagnosis. The technique uses controlled pressure waves traveling in pressurized water pipes for diagnosis, the principal of which is similar to the use of sonar for object detection under water. Over the past three decades, significant advancements have been achieved in the development of hydraulic transient-based techniques, and many field validations have confirmed the effectiveness and advantages. This chapter provides a comprehensive review of hydraulic transient-based pipeline condition assessment and feature diagnosis techniques. The review includes the fundamentals of transient pressure waves in water pipes, conventional transient-based techniques built on detailed physical understanding or sophisticated hydraulic models of pipe systems, more recently developed techniques that leverage machine learning and artificial intelligence, and notable field applications and validations.