diff --git a/packages/distance/src/argmin.ts b/packages/distance/src/argmin.ts
new file mode 100644
index 0000000000..b6cfbdcbfc
--- /dev/null
+++ b/packages/distance/src/argmin.ts
@@ -0,0 +1,94 @@
+import type { Fn } from "@thi.ng/api";
+import type { ReadonlyVec } from "@thi.ng/vectors";
+import type { IDistance } from "./api";
+import { knearest } from "./knearest";
+import { DIST_SQ } from "./squared";
+
+/**
+ * Takes a vector `p`, array of `samples` and a `dist`ance function. Computes
+ * and returns index of the sample closest to `p` (according to `dist`, default:
+ * {@link DIST_SQ}).
+ *
+ * @reference
+ * Use {@link argminT} for non-vector inputs.
+ *
+ * https://en.wikipedia.org/wiki/Arg_max#Arg_min
+ *
+ * @param p
+ * @param samples
+ * @param dist
+ */
+export const argmin = (
+    p: ReadonlyVec,
+    samples: ReadonlyVec[],
+    { metric: dist }: IDistance<ReadonlyVec> = DIST_SQ
+) => {
+    let minD = Infinity;
+    let minArg = -1;
+    for (let i = 0, n = samples.length; i < n; i++) {
+        const d = dist(p, samples[i]);
+        if (d < minD) {
+            minD = d;
+            minArg = i;
+        }
+    }
+    return minArg;
+};
+
+/**
+ * Similar to {@link argmin}, but for non-vector inputs and taking an additional
+ * `key` function. Applies `key` to each item (incl. `p`) to obtain its vector
+ * representation, then computes and returns index of the sample closest to `p`
+ * (according to `dist`).
+ *
+ * @reference
+ * Use {@link argmin} if all items are vectors already.
+ *
+ * @param p
+ * @param samples
+ * @param key
+ * @param dist
+ */
+export const argminT = <T>(
+    p: T,
+    samples: T[],
+    key: Fn<T, ReadonlyVec>,
+    dist?: IDistance<ReadonlyVec>
+) => argmin(key(p), samples.map(key), dist);
+
+/**
+ * Similar to {@link argmin}, but for k-nearest queries.
+ *
+ * @param k
+ * @param p
+ * @param samples
+ * @param dist
+ */
+export const argminK = (
+    k: number,
+    p: ReadonlyVec,
+    samples: ReadonlyVec[],
+    dist: IDistance<ReadonlyVec> = DIST_SQ
+) => {
+    const neighborhood = knearest<number>(p, k, Infinity, dist);
+    for (let i = 0, n = samples.length; i < n; i++) {
+        neighborhood.consider(samples[i], i);
+    }
+    return neighborhood.values();
+};
+
+/**
+ * Similar to {@link argminT}, but for k-nearest queries.
+ *
+ * @param k
+ * @param p
+ * @param samples
+ * @param dist
+ */
+export const argminKT = <T>(
+    k: number,
+    p: T,
+    samples: T[],
+    key: Fn<T, ReadonlyVec>,
+    dist?: IDistance<ReadonlyVec>
+) => argminK(k, key(p), samples.map(key), dist);
diff --git a/packages/distance/src/index.ts b/packages/distance/src/index.ts
index 67644381de..958b7f9659 100644
--- a/packages/distance/src/index.ts
+++ b/packages/distance/src/index.ts
@@ -1,9 +1,8 @@
 export * from "./api";
-
+export * from "./argmin";
 export * from "./eucledian";
 export * from "./haversine";
-export * from "./manhattan";
-export * from "./squared";
-
 export * from "./knearest";
+export * from "./manhattan";
 export * from "./nearest";
+export * from "./squared";