diff --git a/NEWS.md b/NEWS.md
index 3f3121a..e2f4486 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,8 @@
 
 * include the argument `which_plots` in the `plot.lcc` function so that one can
   specify which plots should be plotted
+* include the argument `n_item_consensus` in the `plot.lcc` function so that one
+  can specify how many item consensus plots should be plotted in one plot
 
 # longmixr 1.0.0
 
diff --git a/R/plot.lcc.R b/R/plot.lcc.R
index d66f752..287ddc4 100644
--- a/R/plot.lcc.R
+++ b/R/plot.lcc.R
@@ -9,6 +9,8 @@
 #' of clusters, \code{"CDF"}, \code{"delta"}, \code{"cluster_tracking"},
 #' \code{"item_consensus"} or \code{"cluster_consensus"}. When you want to plot
 #' all consensus matrices and the legend, you can just use \code{"consensusmatrix"}.
+#' @param n_item_consensus determines how many item consensus plots are plotted
+#' together in one plot before a new plot is used; the default is \code{3}.
 #' @param ... additional parameters for plotting; currently not used
 #'
 #' @return Plots the following plots (when selected):\tabular{ll}{
@@ -35,10 +37,12 @@
 plot.lcc <- function(x,
                      color_palette = NULL,
                      which_plots = "all",
+                     n_item_consensus = 3,
                      ...) {
 
   checkmate::assert_class(x, "lcc")
   checkmate::assert_character(color_palette, null.ok = TRUE)
+  checkmate::assert_int(n_item_consensus, lower = 1)
 
   # determine the possible consensus matrices
   possible_consensusmatrix <- paste0("consensusmatrix_",
@@ -178,7 +182,7 @@ plot.lcc <- function(x,
   cci <- rbind()
   sumx <- list()
   colors_arr <- c()
-  old_par <- par(mfrow = c(3, 1), mar = c(4, 3, 2, 0))
+  old_par <- par(mfrow = c(n_item_consensus, 1), mar = c(4, 3, 2, 0))
   on.exit(par(old_par))
   # tk is the number of predefined clusters
   for (tk in seq(from = 2, to = length(x), by = 1)) {
diff --git a/man/plot.lcc.Rd b/man/plot.lcc.Rd
index 473f37f..bffa53c 100644
--- a/man/plot.lcc.Rd
+++ b/man/plot.lcc.Rd
@@ -4,7 +4,7 @@
 \alias{plot.lcc}
 \title{Plot a longitudinal consensus clustering}
 \usage{
-\method{plot}{lcc}(x, color_palette = NULL, which_plots = "all", ...)
+\method{plot}{lcc}(x, color_palette = NULL, which_plots = "all", n_item_consensus = 3, ...)
 }
 \arguments{
 \item{x}{\code{lcc} object (output from \code{\link{longitudinal_consensus_cluster}})}
@@ -13,12 +13,15 @@
 
 \item{which_plots}{determine which plots should be plotted; the default is \code{"all"}.
 Alternatively, a combination of the following values can be specified to plot
-only certain plots of the below mentioned plots: \code{"consensusmatrix_legend"},
+only some of the below mentioned plots: \code{"consensusmatrix_legend"},
 \code{"consensusmatrix_x"} where \code{x} is replaced by the corresponding number
 of clusters, \code{"CDF"}, \code{"delta"}, \code{"cluster_tracking"},
 \code{"item_consensus"} or \code{"cluster_consensus"}. When you want to plot
 all consensus matrices and the legend, you can just use \code{"consensusmatrix"}.}
 
+\item{n_item_consensus}{determines how many item consensus plots are plotted
+together in one plot before a new plot is used; the default is \code{3}.}
+
 \item{...}{additional parameters for plotting; currently not used}
 }
 \value{
diff --git a/tests/testthat/_snaps/lcc_plot/plot_lcc_output_item_consenus_1.png b/tests/testthat/_snaps/lcc_plot/plot_lcc_output_item_consenus_1.png
new file mode 100644
index 0000000..1a577bb
Binary files /dev/null and b/tests/testthat/_snaps/lcc_plot/plot_lcc_output_item_consenus_1.png differ
diff --git a/tests/testthat/test_lcc_plot.R b/tests/testthat/test_lcc_plot.R
index 3b964b3..fc3b917 100644
--- a/tests/testthat/test_lcc_plot.R
+++ b/tests/testthat/test_lcc_plot.R
@@ -73,3 +73,14 @@ test_that("the which_plots argument is correct", {
   expect_snapshot_file(save_png(plot(clustering, which_plots = c("CDF", "cluster_tracking"))),
                        "plot_lcc_output_cdf_cluster_tracking.png")
 })
+
+test_that("the n_item_consensus argument is correct", {
+  expect_error(plot(clustering, n_item_consensus = "test"))
+  expect_error(plot(clustering, n_item_consensus = -1))
+
+  skip_on_ci()
+  # only the last plot (with k=3) is recorded
+  expect_snapshot_file(save_png(plot(clustering, which_plots = "item_consensus",
+                                     n_item_consensus = 1)),
+                       "plot_lcc_output_item_consenus_1.png")
+})