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diff --git a/epclust/tests/testthat/test-assignMedoids.R b/epclust/tests/testthat/test-assignMedoids.R
new file mode 100644
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--- /dev/null
+++ b/epclust/tests/testthat/test-assignMedoids.R
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+context("assignMedoids")
+
+test_that("assignMedoids behave as expected",
+{
+	# Generate a gaussian mixture
+	n = 999
+	L = 7
+	medoids = cbind( rep(0,L), rep(-5,L), rep(5,L) )
+	# short series...
+	series = t( rbind( MASS::mvrnorm(n/3, medoids[,1], diag(L)),
+		MASS::mvrnorm(n/3, medoids[,2], diag(L)),
+		MASS::mvrnorm(n/3, medoids[,3], diag(L)) ) )
+
+	# With high probability, medoids indices should resemble 1,1,1,...,2,2,2,...,3,3,3,...
+	mi = epclust:::assignMedoids(medoids, series)
+	mi_ref = rep(1:3, each=n/3)
+	expect_lt( mean(mi != mi_ref), 0.01 )
+
+	# Now with a random matrix, compare with (~trusted) R version
+	series = matrix(runif(n*L, min=-7, max=7), nrow=L)
+	mi = epclust:::assignMedoids(medoids, series)
+	mi_ref = R_assignMedoids(medoids, series)
+	expect_equal(mi, mi_ref)
+})
+
+# R-equivalent of , requiring a matrix
+# (thus potentially breaking "fit-in-memory" hope)
+R_assignMedoids <- function(medoids, series)
+{
+	nb_series = ncol(series) #series in columns
+
+	mi = rep(NA,nb_series)
+	for (i in 1:nb_series)
+		mi[i] <- which.min( colSums( sweep(medoids, 1, series[,i], '-')^2 ) )
+
+	mi
+}