Regime transitions in vertical gas-liquid flow are read from mechanistic models or assigned by eye, and for churn flow neither route has produced a settled criterion. We ask how much an unsupervised topological description of flow video can establish about them. The description is the Euler characteristic surface, a count of the connected gas and liquid regions in each frame across morphological scales. Kernels derived from it are combined by unsupervised multiple kernel learning, and the trials are divided into contiguous groups along gas velocity. For 37 air-water trials in 2-in. tubing at one liquid rate, with three viewing stations averaged, four groups are separated at 4.33, 7.51 and 11.70 m/s. Each division falls at or within one trial of a change in the visual evaluation of the same videos; cuts placed at random would match as closely in 12% of cases. The divisions move between viewing stations and under resampling, a kernel built on gas velocity divides any sampled range into roughly equal parts and cannot locate a transition, and the data do not support a revised transition criterion. On independent images from an annulus, churn shows about three times the across-scale variance of slug. Within the band the published criteria assign to churn, the two upper cuts fall at graded changes in the rankings, consistent with a progression within churn; if confirmed by further data, they would subdivide the churn region.

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